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.