* Knowledge of computer networking concepts and protocols, and network security methodologies.

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Consult with customers and key stakeholders to evaluate functional requirements for AI and data applications.

Correlates training and learning to business or mission requirements.

Develop new or identify existing awareness and training materials that are appropriate for intended audiences.

Ability to prepare and deliver education and awareness briefings to ensure that systems, network, and data users are aware of and adhere to systems security policies and procedures.

Ensure that AI design and development activities are properly documented and updated.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Ability to communicate complex information, concepts, or ideas in a confident and well-organized manner through verbal, written, and/or visual means.

Gather feedback on customer satisfaction and internal service performance to foster continual improvement.

Present technical information to technical and non-technical audiences.

Analyze national security/DoD mission priorities and gaps suitable for the application of AI solutions.

Coordinate with change management employees to plan, foster, and track change.

Identify viable AI projects based on organizational needs.

Identify ways to lead and motivate people to adopt AI solutions through cultural, organizational, or other types of change.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Promote awareness of AI limitations and benefits.

Support an AI adoption strategy that aligns with the organization’s vision, mission, and goals.

Test how users interact with AI solutions.

Knowledge of machine learning theory and principles.

* Knowledge of specific operational impacts of cybersecurity lapses.

Skill in communicating with all levels of the organization, including senior/mid-level executives, and operational-level personnel (e.g., interpersonal skills, approachability, effective listening skills, appropriate use of style and language for the audience).

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Ability to inspire and lead a culture of innovation.

Knowledge of AI security risks, threats, and vulnerabilities and potential risk mitigation solutions.

Knowledge of change models and frameworks.

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of emerging trends and future use cases of AI.

Knowledge of how AI is developed and operated.

Knowledge of how humans interact with and/or are impacted by AI solutions within the DoD context.

Knowledge of how to structure and display data.

Knowledge of how to use data to tell a story.

Knowledge of human factor engineering.

Knowledge of machine learning operations (MLOps) processes and best practices.

Knowledge of the AI lifecycle.

Knowledge of the basic requirements for the successful delivery of AI solutions.

Knowledge of the basics of customer experience, customer design, psychology of customer decision-making, and human-computer interaction.

Knowledge of the benefits and limitations of AI capabilities.

Knowledge of the possible impacts of machine learning blind spots and edge cases.

Knowledge of the user experience (e.g., decision making, user design, and human-computer interaction) as it relates to AI systems.

Skill in communicating AI and/or machine learning solutions to a wide range of audiences.

Skill in explaining AI concepts and terminology.

Skill in leading AI adoption efforts.

Knowledge of the organization’s core business/mission processes.

Use knowledge of business processes to create or recommend AI solutions.

Coordinate with change management employees to plan, foster, and track change.

Engage and collaborate with allies and partners to advance shared strategic AI objectives.

Knowledge of principles and processes for conducting training and education needs assessment.

Knowledge of customer mission priorities and capabilities, as related to the integration and adoption of AI solutions.

Knowledge of human factor engineering.

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

Knowledge of remedies against unintended bias in AI solutions.

* Knowledge of computer networking concepts and protocols, and network security methodologies.

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Acquire and manage the necessary resources, including leadership support, financial resources, infrastructure, and key personnel, to support AI innovation adoption goals and objectives.

Advise senior management on risk levels, security posture, and necessary changes to existing AI policies.​

Design and integrate an AI adoption strategy that supports the organization’s vision, mission, and goals.

Develop and maintain strategic plans.

Identify and address AI workforce planning and management issues (e.g., recruitment, retention, and training).

Oversee AI budget, staffing, and contracting decisions.

Knowledge of the organization’s core business/mission processes.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Facilitate interactions between internal and external partner decision makers to synchronize and integrate courses of action in support of objectives.

Maintain relationships with internal and external partners involved in cyber planning or related areas.

Conduct long-range, strategic planning efforts with internal and external partners to support AI capability development and use.

Knowledge of organization objectives, leadership priorities, and decision-making risks.

Analyze national security/DoD mission priorities and gaps suitable for the application of AI solutions.

Appoint and guide a multidisciplinary team of AI experts to identify and assess risk throughout the AI development lifecycle.

Assess value of implemented AI projects based on organizational metrics.

Create and/or maintain governance structure for oversight and accountability of AI solutions.

Direct and/or support organizational and project-level AI risk management activities.

Engage and collaborate with allies and partners to advance shared strategic AI objectives.

Establish and/or maintain processes to ensure Responsible AI practices are reflected in an organization’s approach to AI acquisition, development, and deployment.

Evaluate and develop AI workforce structure resources and requirements.

Identify and address key roadblocks to AI implementation.

Identify viable AI projects based on organizational needs.

Identify ways to lead and motivate people to adopt AI solutions through cultural, organizational, or other types of change.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Promote awareness of AI limitations and benefits.

Remove barriers to data acquisition, collection, and curation efforts required for AI solutions.

Ability to assess and forecast manpower requirements to meet organizational objectives.

Knowledge of Workforce Framework, work roles, and associated tasks, knowledge, skills, and abilities.

Knowledge of machine learning theory and principles.

* Knowledge of specific operational impacts of cybersecurity lapses.

Skill in communicating with all levels of the organization, including senior/mid-level executives, and operational-level personnel (e.g., interpersonal skills, approachability, effective listening skills, appropriate use of style and language for the audience).

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Ability to identify, connect, and influence key stakeholders to speed AI adoption.

Ability to inspire and lead a culture of innovation.

Knowledge of AI security risks, threats, and vulnerabilities and potential risk mitigation solutions.

Knowledge of best practices in organizational conflict management.

Knowledge of data acquisition, collection, and curation best practices required for AI solutions.

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of emerging trends and future use cases of AI.

Knowledge of how AI is developed and operated.

Knowledge of interactions and integration of DataOps, MLOps, and DevSecOps in AI.

Knowledge of resources and capabilities required to complete AI projects.

Knowledge of staffing, contracting, and budgetary requirements to run an AI-enabled organization.

Knowledge of the AI lifecycle.

Knowledge of the basic requirements for the successful delivery of AI solutions.

Knowledge of the benefits and limitations of AI capabilities.

Knowledge of the nature and function of technology platforms and tools used to create and employ AI.

Skill in communicating AI and/or machine learning solutions to a wide range of audiences.

Skill in developing and influencing policy, plans, and strategy in compliance with laws, regulations, policies, and standards in support of organizational AI activities.

Skill in explaining AI concepts and terminology.

Skill in identifying organizational and project-level AI risks, including AI security risks and requirements.

Skill in leading AI adoption efforts.

Skill in leveraging and optimizing resources required to complete AI projects and programs.

Knowledge of both internal and external customers and partner organizations, including information needs, objectives, structure, capabilities, etc.

Knowledge of organization policies and planning concepts for partnering with internal and/or external organizations.

Establish and collect metrics to monitor and validate AI workforce readiness.

Define and/or implement policies and procedures to enable an AI risk assessment process and assess risk mitigation efforts.

Monitor and evaluate the organization’s use of AI to ensure capabilities are performing as intended and to reduce the likelihood and severity of unintended consequences.

Recommend updates to military strategy and doctrine with respect to advances in AI technology, legal obligations, Responsible AI, and DoD AI Ethical Principles.

Knowledge of how to leverage government research and development centers, think tanks, academic research, and industry systems.

Knowledge of AI-specific acquisition models (e.g., pay per use or per data element).

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

Knowledge of metrics to evaluate the effectiveness of machine learning models.

Knowledge of organization’s structure, training requirements, and existing operational hardware/software related to the AI solution to be adopted.

Knowledge of remedies against unintended bias in AI solutions.

Knowledge of the possible impacts of machine learning blind spots and edge cases.

* Knowledge of computer networking concepts and protocols, and network security methodologies.

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Develop methods to monitor and measure risk and assurance efforts on a continuous basis.

Perform AI architecture security reviews, identify gaps, and develop a risk management plan to address issues.

Knowledge of emerging security issues, risks, and vulnerabilities.

Ensure risk mitigation plans of action and milestones are in place.

Ensure that AI design and development activities are properly documented and updated.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data resusability concerns for AI solutions.

Communicate the results of AI risk assessments to relevant stakeholders.

Coordinate with appropriate personnel to identify methods for users and developers to report concerns about the implementation of DoD AI Ethical Principles.

Create and/or maintain processes to ensure data management efforts comply with AI ethical principles.

Determine methods and metrics for quantitative and qualitative measurement of AI risks so that sensitivity, specificity, likelihood, confidence levels, and other metrics are identified, documented, and applied.

Develop risk mitigation strategies to ensure enumerated risks are prioritized, mitigated, shared, transferred, and/or accepted.

Direct and/or support organizational and project-level AI risk management activities.

Ensure risk management responsibilities are clearly defined, assigned, and communicated to relevant stakeholders.

Identify and submit exemplary AI use cases, best practices, failure modes, and risk mitigation strategies, including after-action reports.

Implement Responsible AI best practices and standards within AI solutions according to the DoD AI Ethical Principles, Responsible AI Guidelines, and/or any other pertinent laws.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Measure the compliance of AI tools with DoD AI Ethical Principles.

Perform risk assessment on AI applications to identify technical, societal, organizational, and mission risks.

Knowledge of machine learning theory and principles.

* Knowledge of specific operational impacts of cybersecurity lapses.

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Knowledge of AI security risks, threats, and vulnerabilities and potential risk mitigation solutions.

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of emerging trends and future use cases of AI.

Knowledge of how AI is developed and operated.

Knowledge of interactions and integration of DataOps, MLOps, and DevSecOps in AI.

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

Knowledge of metrics to evaluate the effectiveness of machine learning models.

Knowledge of Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability considerations for AI solutions.

Knowledge of remedies against unintended bias in AI solutions.

Knowledge of the AI lifecycle.

Knowledge of the benefits and limitations of AI capabilities.

Knowledge of the possible impacts of machine learning blind spots and edge cases.

Knowledge of the principles, methods, and tools used for risk and bias assessment and mitigation, including assessment of failures and their consequences.

Skill in assessing AI capabilities for bias or ethical concerns.

Skill in developing solutions and/or recommendations to minimize negative impacts of machine learning, especially for edge cases.

Skill in explaining AI concepts and terminology.

Skill in identifying low-probability, high-impact risks in machine learning training data sets.

Skill in identifying organizational and project-level AI risks, including AI security risks and requirements.

Skill in identifying risk over the lifespan of an AI solution.

Skill in testing and evaluating machine learning algorithms or AI solutions.

Knowledge of the organization’s core business/mission processes.

Perform risk assessment whenever an AI application or AI-enabled system undergoes a major change, when emergent behaviors are detected, and/or unintended consequences are reported.

Knowledge of testing, evaluation, validation, and verification (T&E V&V) tools and procedures to ensure systems are working as intended.

* Knowledge of computer networking concepts and protocols, and network security methodologies.

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Skill in determining an appropriate level of test rigor for a given system.

Determine level of assurance of developed capabilities based on test results.

Develop test plans to address specifications and requirements.

Make recommendations based on test results.

Test, evaluate, and verify hardware and/or software to determine compliance with defined specifications and requirements.

Record and manage test data.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Conduct hypothesis testing using statistical processes.

Assess technical risks and limitations of planned tests on AI systems.

Build assurance cases for AI systems that support the needs of different stakeholders (e.g., acquisition community, commanders, and operators).

Conduct AI risk assessments to ensure models and/or other solutions are performing as designed.

Create or customize existing Test and Evaluation Master Plans (TEMPs) for AI systems.

Determine methods and metrics for quantitative and qualitative measurement of AI risks so that sensitivity, specificity, likelihood, confidence levels, and other metrics are identified, documented, and applied.

Develop machine learning code testing and validation procedures.

Develop possible solutions for technical risks and limitations of planned tests on AI solutions.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Measure the effectiveness, security, robustness, and trustworthiness of AI tools.

Provide quality assurance of AI products throughout their lifecycle.

Report test and evaluation deficiencies and possible solutions to appropriate personnel.

Select and use the appropriate models and prediction methods for evaluating AI performance.

Test AI tools against adversarial attacks in operationally realistic environments.

Test components to ensure they work as intended in a variety of scenarios for all aspects of the AI application.

Test how users interact with AI solutions.

Test the reliability, functionality, security, and compatibility of AI tools within systems.

Test the trustworthiness of AI solutions.

Use models and other methods for evaluating AI performance.

Ability to collect, verify, and validate test data.

Ability to translate data and test results into evaluative conclusions.

Knowledge of machine learning theory and principles.

Skill in assessing the predictive power and subsequent generalizability of a model.

Skill in preparing Test & Evaluation reports.

Skill in providing Test & Evaluation resource estimate.

* Knowledge of specific operational impacts of cybersecurity lapses.

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Knowledge of AI security risks, threats, and vulnerabilities and potential risk mitigation solutions.

Knowledge of AI Test & Evaluation frameworks.

Knowledge of best practices from industry and academia in test design activities for verification and validation of AI and machine learning systems.

Knowledge of coding and scripting in languages that support AI development and use.

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of how AI is developed and operated.

Knowledge of how AI solutions integrate with cloud or other IT infrastructure.

Knowledge of how to automate development, testing, security, and deployment of AI/machine learning-enabled software to the DoD.

Knowledge of how to collect, store, and monitor data.

Knowledge of how to deploy test infrastructures with AI systems.

Knowledge of interactions and integration of DataOps, MLOps, and DevSecOps in AI.

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

Knowledge of machine learning operations (MLOps) processes and best practices.

Knowledge of metrics to evaluate the effectiveness of machine learning models.

Knowledge of remedies against unintended bias in AI solutions.

Knowledge of testing, evaluation, validation, and verification (T&E V&V) tools and procedures to ensure systems are working as intended.

Knowledge of the AI lifecycle.

Knowledge of the benefits and limitations of AI capabilities.

Knowledge of the possible impacts of machine learning blind spots and edge cases.

Knowledge of the user experience (e.g., decision making, user design, and human-computer interaction) as it relates to AI systems.

Knowledge of tools for testing the robustness and resilience of AI products and solutions.

Skill in explaining AI concepts and terminology.

Skill in identifying low-probability, high-impact risks in machine learning training data sets.

Skill in identifying risk over the lifespan of an AI solution.

Skill in integrating AI Test & Evaluation frameworks into test strategies for specific projects.

Skill in testing and evaluating machine learning algorithms or AI solutions.

Skill in testing for bias in data sets and AI system outputs as well as determining historically or often underrepresented and marginalized groups are properly represented in the training, testing, and validation data sets and AI system outputs.

Skill in translating operation requirements for AI systems into testing requirements.

Knowledge of organization’s evaluation and validation requirements.

Perform AI architecture security reviews, identify gaps, and develop a risk management plan to address issues.

Knowledge of the organization’s core business/mission processes.

Knowledge of service management concepts for networks and related standards (e.g., Information Technology Infrastructure Library, current version [ITIL]).

Assist integrated project teams to identify, curate, and manage data.

Identify and submit exemplary AI use cases, best practices, failure modes, and risk mitigation strategies, including after-action reports.

Knowledge of current test standards and safety standards that are applicable to AI (e.g. MIL-STD 882E, DO-178C, ISO26262).

Knowledge of Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability considerations for AI solutions.

Knowledge of computer algorithms.

* Knowledge of computer networking concepts and protocols, and network security methodologies.

Knowledge of mathematics, including logarithms, trigonometry, linear algebra, calculus, statistics, and operational analysis.

Knowledge of programming language structures and logic.

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Skill in conducting queries and developing algorithms to analyze data structures.

Correct errors by making appropriate changes and rechecking the program to ensure desired results are produced.

Design, develop, and modify software systems, using scientific analysis and mathematical models to predict and measure outcome and consequences of design.

Develop secure code and error handling.

Perform secure programming and identify potential flaws in codes to mitigate vulnerabilities.

Ensure that AI design and development activities are properly documented and updated.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Conduct hypothesis testing using statistical processes.

Assess and address the limitations of methods to deliver machine learning models.

Conduct AI risk assessments to ensure models and/or other solutions are performing as designed.

Design and develop machine learning models to achieve organizational objectives.

Design, develop, and implement AI tools and techniques to achieve organizational objectives.

Determine methods and metrics for quantitative and qualitative measurement of AI risks so that sensitivity, specificity, likelihood, confidence levels, and other metrics are identified, documented, and applied.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Research the latest machine learning and AI tools, techniques, and best practices.

Use models and other methods for evaluating AI performance.

Write and document reproducible code.

Ability to collect, verify, and validate test data.

Knowledge of machine learning theory and principles.

Skill in writing scripts using R, Python, PIG, HIVE, SQL, etc.

* Knowledge of specific operational impacts of cybersecurity lapses.

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Knowledge of coding and scripting in languages that support AI development and use.

Knowledge of current AI and machine learning systems design and performance analysis models, algorithms, and tools.

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of how AI is developed and operated.

Knowledge of how to automate development, testing, security, and deployment of AI/machine learning-enabled software to the DoD.

Knowledge of how to collect, store, and monitor data.

Knowledge of how to structure and display data.

Knowledge of how to use data to tell a story.

Knowledge of machine learning operations (MLOps) processes and best practices.

Knowledge of metrics to evaluate the effectiveness of machine learning models.

Knowledge of the AI lifecycle.

Knowledge of the basic requirements for the successful delivery of AI solutions.

Knowledge of the benefits and limitations of AI capabilities.

Knowledge of the latest machine learning and AI tools, techniques, and best practices.

Knowledge of the nature and function of technology platforms and tools used to create and employ AI.

Knowledge of the possible impacts of machine learning blind spots and edge cases.

Skill in analyzing the output from machine learning models.

Skill in building and deploying machine learning models.

Skill in creating machine learning models.

Skill in explaining AI concepts and terminology.

Skill in identifying low-probability, high-impact risks in machine learning training data sets.

Skill in testing and evaluating machine learning algorithms or AI solutions.

Knowledge of the organization’s core business/mission processes.

Use knowledge of business processes to create or recommend AI solutions.

Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data resusability concerns for AI solutions.

Consider energy implications (graphical processing unit, tensor processing unit, etc.) when designing AI solutions.

Design and develop continuous integration/continuous delivery (CI/CD) in a containerized or other reproducible computing environment to support the machine learning life cycle.

Identify and submit exemplary AI use cases, best practices, failure modes, and risk mitigation strategies, including after-action reports.

Implement Responsible AI best practices and standards within AI solutions according to the DoD AI Ethical Principles, Responsible AI Guidelines, and/or any other pertinent laws.

Knowledge of how to leverage government research and development centers, think tanks, academic research, and industry systems.

Knowledge of AI security risks, threats, and vulnerabilities and potential risk mitigation solutions.

Knowledge of emerging trends and future use cases of AI.

Knowledge of how AI adoption can assist developers with service-oriented design.

Knowledge of how AI solutions integrate with cloud or other IT infrastructure.

Knowledge of how commercial and federal solutions solve Defense-related data environment and platform challenges.

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

Knowledge of Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability considerations for AI solutions.

Knowledge of remedies against unintended bias in AI solutions.

Knowledge of testing, evaluation, validation, and verification (T&E V&V) tools and procedures to ensure systems are working as intended.

Skill in identifying risk over the lifespan of an AI solution.

Skill in labeling data to make it more discoverable and understandable.

* Knowledge of computer networking concepts and protocols, and network security methodologies.

Knowledge of data mining and data warehousing principles.

Knowledge of query languages such as SQL (structured query language).

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Skill in conducting queries and developing algorithms to analyze data structures.

Skill in generating queries and reports.

Ability to interpret and incorporate data from multiple tool sources.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Ability to communicate complex information, concepts, or ideas in a confident and well-organized manner through verbal, written, and/or visual means.

Analyze data sources to provide actionable recommendations.

Collect metrics and trending data.

Develop strategic insights from large data sets.

Present technical information to technical and non-technical audiences.

Manipulate and clean large, disparate datasets for bulk analysis to identify connections.

Ability to identify basic common coding flaws at a high level.

Ability to use data visualization tools (e.g., Flare, HighCharts, AmCharts, D3.js, Processing, Google Visualization API, Tableau, Raphael.js).

Knowledge of how to utilize Hadoop, Java, Python, SQL, Hive, and PIG to explore data.

Read, interpret, write, modify, and execute scripts, macros, and functions.

Skill in identifying hidden patterns or relationships.

Skill in using basic descriptive statistics and techniques (e.g., normality, model distribution, scatter plots).

Skill in using data analysis tools (e.g., Excel, STATA SAS, SPSS).

Utilize different programming languages to write code, open files, read files, and write output to different files.

* Knowledge of specific operational impacts of cybersecurity lapses.

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Knowledge of how to collect, store, and monitor data.

Knowledge of how to structure and display data.

Knowledge of how to use data to tell a story.

Provide a managed flow of relevant information (via web-based portals or other means) based on a mission requirements.

Knowledge of the organization’s core business/mission processes.

Knowledge of Personally Identifiable Information (PII) data security standards.

Knowledge of Personal Health Information (PHI) data security standards.

Analyze data sources to provide actionable recommendations.

Present data in creative formats.

Provide actionable recommendations to critical stakeholders based on data analysis and findings.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Skill in communicating with all levels of the organization, including senior/mid-level executives, and operational-level personnel (e.g., interpersonal skills, approachability, effective listening skills, appropriate use of style and language for the audience).

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

* Knowledge of computer networking concepts and protocols, and network security methodologies.

Knowledge of data administration and data standardization policies and standards.

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Knowledge of the capabilities and functionality associated with various technologies for organizing and managing information (e.g., databases, bookmarking engines).

Knowledge of the characteristics of physical and virtual data storage media.

Skill in developing data models.

Analyze and plan for anticipated changes in data capacity requirements.

Analyze information to determine, recommend, and plan the development of a new application or modification of an existing application.

Consult with customers and key stakeholders to evaluate functional requirements for AI and data applications.

Provide recommendations on new database technologies and architectures.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Confer with systems analysts, engineers, programmers and others to design application.

Advise higher level leadership on critical data management issues.

Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data resusability concerns for AI solutions.

Examine and identify database structural necessities by evaluating operations, applications, and programming.

Prepare database design and architecture reports.

Effectively allocate storage capacity in the design of data management systems.

* Knowledge of specific operational impacts of cybersecurity lapses.

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Knowledge of data operations (DataOps) processes and best practices.

Knowledge of how to collect, store, and monitor data.

Skill in designing the best approach and architecture for automated data labeling and data lifecycle.

Knowledge of data administration and data standardization policies and standards.

Knowledge of network access, identity, and access management (e.g., public key infrastructure [PKI]).

Knowledge of how information needs and collection requirements are translated, tracked, and prioritized across the extended enterprise.

Knowledge of the organization’s core business/mission processes.

Knowledge of emerging security issues, risks, and vulnerabilities.

Knowledge of Personally Identifiable Information (PII) data security standards.

Knowledge of Personal Health Information (PHI) data security standards.

Knowledge of service management concepts for networks and related standards (e.g., Information Technology Infrastructure Library, current version [ITIL]).

Knowledge of an organization’s information classification program and procedures for information compromise.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Skill in developing machine understandable semantic ontologies.

Knowledge of container orchestration and resource management platforms.

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of how AI solutions integrate with cloud or other IT infrastructure.

Knowledge of how commercial and federal solutions solve Defense-related data environment and platform challenges.

Knowledge of how to automate development, testing, security, and deployment of AI/machine learning-enabled software to the DoD.

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

* Knowledge of computer networking concepts and protocols, and network security methodologies.

Knowledge of network access, identity, and access management (e.g., public key infrastructure [PKI]).

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Knowledge of sources, characteristics, and uses of the organization’s data assets.

Knowledge of how information needs and collection requirements are translated, tracked, and prioritized across the extended enterprise.

Develop and maintain strategic plans.

Develop data standards, policies, and procedures.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Facilitate interactions between internal and external partner decision makers to synchronize and integrate courses of action in support of objectives.

Knowledge of organization objectives, leadership priorities, and decision-making risks.

Advise higher level leadership on critical data management issues.

Analyze existing and planned data investments to ensure they address key business problems, are compatible with the organization’s mission, and align with the target data architecture.

Create policies for effective data management (e.g., data sharing agreements and security policies).

Demonstrate to executive stakeholders how data and analytics initiatives address agency challenges.

Develop a data management strategy that helps to prioritize investments and resource allocations (e.g., data analytics, data infrastructure).

Develop an organizational change management plan to support a data management strategy.

Facilitate cross-sharing of best practices for data usage.

Lead the development and documentation of solutions for assigned data analytical objectives and projects.

Lead the improvement of data system design processes that affect the success and continuation of key programs.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Manage risk to the data program.

Oversee the management of data classification and handling requirements.

Remove barriers to data acquisition, collection, and curation efforts required for AI solutions.

Set strategic priorities by leveraging data insights.

Ability to assess and forecast manpower requirements to meet organizational objectives.

* Knowledge of specific operational impacts of cybersecurity lapses.

Skill in communicating with all levels of the organization, including senior/mid-level executives, and operational-level personnel (e.g., interpersonal skills, approachability, effective listening skills, appropriate use of style and language for the audience).

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Ability to inspire and lead a culture of innovation.

Knowledge of data acquisition, collection, and curation best practices required for AI solutions.

Knowledge of data architecture and data services implementation.

Knowledge of data model development (e.g., conceptual, logical, and physical).

Knowledge of data security roles and responsibilities.

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of how to collect, store, and monitor data.

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

Skill in developing enterprise-level/Agency-level policies.

Skill in performing strategic-level analysis to develop Enterprise Data Management (EDM) strategies.

Ability to measure human systems interaction (usability, workload, system trust).

Ability to understand technology, management, and leadership issues related to organization processes and problem solving.

Oversee the evaluation of contracts to ensure compliance with funding, legal, and program requirements.

Knowledge of the organization’s core business/mission processes.

Ensure all acquisitions, procurements, and outsourcing efforts address information security requirements consistent with organization goals.

Maintain relationships with internal and external partners involved in cyber planning or related areas.

Knowledge of both internal and external customers and partner organizations, including information needs, objectives, structure, capabilities, etc.

Knowledge of organization policies and planning concepts for partnering with internal and/or external organizations.

Knowledge of Workforce Framework, work roles, and associated tasks, knowledge, skills, and abilities.

Knowledge of how to leverage government research and development centers, think tanks, academic research, and industry systems.

* Knowledge of computer networking concepts and protocols, and network security methodologies.

Knowledge of data administration and data standardization policies and standards.

Knowledge of data mining and data warehousing principles.

Knowledge of database management systems, query languages, table relationships, and views.

Knowledge of query languages such as SQL (structured query language).

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Skill in establishing data security controls.

Skill in developing data dictionaries.

Implement data management standards, requirements, and specifications.

Develop and implement data mining and data warehousing programs.

Develop secure code and error handling.

Manage the compilation, cataloging, caching, distribution, and retrieval of data.

Perform secure programming and identify potential flaws in codes to mitigate vulnerabilities.

Record and manage test data.

Knowledge of Java-based database access application programming interface (API) (e.g., Java Database Connectivity [JDBC]).

Knowledge of database access application programming interfaces (APIs) (e.g., Java Database Connectivity [JDBC]).

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Skill in data mining techniques (e.g., searching file systems) and analysis.

Program custom algorithms.

Advise higher level leadership on critical data management issues.

Apply data acquisition, cleaning, transformation, and ingestion best practices for machine learning data conduits.

Assess and address the limitations of methods to deliver data.

Assist integrated project teams to identify, curate, and manage data.

Build automated data management conduits.

Comply with data classification and handling requirements through access control and security best practices.

Manipulate and clean large, disparate datasets for bulk analysis to identify connections.

Ability to collect, verify, and validate test data.

Knowledge of how to utilize Hadoop, Java, Python, SQL, Hive, and PIG to explore data.

Read, interpret, write, modify, and execute simple scripts (e.g., PERL, VBS) on Windows and UNIX systems (e.g., those that perform tasks such as: parsing large data files, automating manual tasks, and fetching/processing remote data).

Skill in data pre-processing (e.g., imputation, dimensionality reduction, normalization, transformation, extraction, filtering, smoothing).

Skill in performing format conversions to create a standard representation of the data.

Skill in transformation analytics (e.g., aggregation, enrichment, processing).

Skill in using data mapping tools.

Skill in writing scripts using R, Python, PIG, HIVE, SQL, etc.

* Knowledge of specific operational impacts of cybersecurity lapses.

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Knowledge of data operations (DataOps) processes and best practices.

Knowledge of data security roles and responsibilities.

Knowledge of how to collect, store, and monitor data.

Skill in developing and maintaining automation scripts.

Skill in identifying data acquisition, collection, and curation risks.

Implement data mining and data warehousing applications.

Knowledge of the organization’s core business/mission processes.

Knowledge of Personally Identifiable Information (PII) data security standards.

Knowledge of Personal Health Information (PHI) data security standards.

Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data resusability concerns for AI solutions.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Knowledge of container orchestration and resource management platforms.

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of how AI solutions integrate with cloud or other IT infrastructure.

Knowledge of how to automate development, testing, security, and deployment of AI/machine learning-enabled software to the DoD.

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

Knowledge of statistical/machine learning algorithms.

* Knowledge of computer networking concepts and protocols, and network security methodologies.

Knowledge of mathematics, including logarithms, trigonometry, linear algebra, calculus, statistics, and operational analysis.

Knowledge of programming language structures and logic.

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Skill in conducting queries and developing algorithms to analyze data structures.

Skill in creating and utilizing mathematical or statistical models.

Ability to interpret and incorporate data from multiple tool sources.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Ability to use and understand complex mathematical concepts (e.g., discrete math).

Skill in developing or recommending analytic approaches or solutions to problems and situations for which information is incomplete or for which no precedent exists.

Analyze data sources to provide actionable recommendations.

Conduct hypothesis testing using statistical processes.

Program custom algorithms.

Utilize technical documentation or resources to implement a new mathematical, data science, or computer science method.

Build predictive, prescriptive, or descriptive models in collaboration with stakeholders.

Plan and conduct complex analytical, mathematical, and statistical research that informs operational requirements.

Plan, coordinate, and execute complex studies using advanced data modeling techniques and procedures, data trend analysis, and data algorithms.

Train and evaluate machine learning models.

Write and document reproducible code.

Ability to build complex data structures and high-level programming languages.

Ability to collect, verify, and validate test data.

Ability to dissect a problem and examine the interrelationships between data that may appear unrelated.

Skill in assessing the predictive power and subsequent generalizability of a model.

Skill in identifying hidden patterns or relationships.

Skill in Regression Analysis (e.g., Hierarchical Stepwise, Generalized Linear Model, Ordinary Least Squares, Tree-Based Methods, Logistic).

Skill in using outlier identification and removal techniques.

Skill in writing scripts using R, Python, PIG, HIVE, SQL, etc.

Utilize open source languages, as appropriate, and apply quantitative techniques (e.g., descriptive and inferential statistics, sampling, experimental design, parametric and non-parametric tests of difference, ordinary least squares regression, general line).

* Knowledge of specific operational impacts of cybersecurity lapses.

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Assist integrated project teams identify, curate, and manage test data.

Knowledge of how to collect, store, and monitor data.

Skill in labeling data to make it more discoverable and understandable.

Knowledge of digital rights management.

Design, develop, and modify software systems, using scientific analysis and mathematical models to predict and measure outcome and consequences of design.

Knowledge of the organization’s core business/mission processes.

Knowledge of Personally Identifiable Information (PII) data security standards.

Knowledge of Personal Health Information (PHI) data security standards.

Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data resusability concerns for AI solutions.

Evaluate energy implications (graphical processing unit, tensor processing unit, etc.) when designing AI solutions.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Plan, coordinate, and execute complex studies using advanced data modeling techniques and procedures, data trend analysis, and data algorithms.

Knowledge of how to leverage government research and development centers, think tanks, academic research, and industry systems.

Skill in Regression Analysis (e.g., Hierarchical Stepwise, Generalized Linear Model, Ordinary Least Squares, Tree-Based Methods, Logistic).

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

Skill in using deep learning approaches to build machine learning models.

* Knowledge of computer networking concepts and protocols, and network security methodologies.

Knowledge of data administration and data standardization policies and standards.

* Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).

Skill in developing data dictionaries.

Implement data management standards, requirements, and specifications.

Analyze and define data requirements and specifications.

Manage the compilation, cataloging, caching, distribution, and retrieval of data.

Ability to prepare and deliver education and awareness briefings to ensure that systems, network, and data users are aware of and adhere to systems security policies and procedures.

Knowledge of Personally Identifiable Information (PII) data security standards.

Knowledge of Personal Health Information (PHI) data security standards.

* Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity.

* Knowledge of cybersecurity principles.

* Knowledge of cyber threats and vulnerabilities.

Assess the validity of source data and subsequent findings.

Review feedback on customer satisfaction and internal service performance to foster continual improvement.

Assist integrated project teams to identify, curate, and manage data.

Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data resusability concerns for AI solutions.

Collaborate with data owners to establish data quality rules and definitions.

Create data catalogs and dictionaries.

Create metrics that characterize the usability, timeliness, completeness, and accuracy of data for multiple users to reference and use.

Identify and document customer requirements when on-boarding new data assets.

Manage compliance with data classification and handling requirements.

Recommend data collection, integration, and retention requirements.

Ability to collect, verify, and validate test data.

* Knowledge of specific operational impacts of cybersecurity lapses.

Skill in communicating with all levels of the organization, including senior/mid-level executives, and operational-level personnel (e.g., interpersonal skills, approachability, effective listening skills, appropriate use of style and language for the audience).

* Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS).

* Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments.

Knowledge of data protection standards and frameworks to prevent unauthorized access to data, and safeguard against unauthorized disclosure of data.

Knowledge of data security roles and responsibilities.

Knowledge of how to collect, store, and monitor data.

Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government.

Knowledge of Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability considerations for AI solutions.

Skill in labeling data to make it more discoverable and understandable.

Knowledge of how information needs and collection requirements are translated, tracked, and prioritized across the extended enterprise.

Consult with customers and key stakeholders to evaluate functional requirements for AI and data applications.

Knowledge of the organization’s core business/mission processes.

Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI.

Skill in developing machine understandable semantic ontologies.

Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable).

Knowledge of key decision-support needs and questions to drive prioritization of data efforts.