* 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 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.

* Knowledge of specific operational impacts of cybersecurity lapses.

Knowledge of computer algorithms.

Knowledge of computer programming principles such as object-oriented design.

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 digital rights management.

Knowledge of enterprise messaging systems and associated software.

Knowledge of Information Theory (e.g., source coding, channel coding, algorithm complexity theory, and data compression).

Knowledge of low-level computer languages (e.g., assembly languages).

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

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

Knowledge of operating systems.

Knowledge of policy-based and risk adaptive access controls.

Knowledge of programming language structures and logic.

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

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

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

Skill in creating and utilizing mathematical or statistical models.

Skill in developing data dictionaries.

Skill in developing data models.

Skill in the use of design modeling (e.g., unified modeling language).

Skill in writing code in a currently supported programming language (e.g., Java, C++).

Knowledge of Unix command line (e.g., mkdir, mv, ls, passwd, grep).

Analyze and define data requirements and specifications.

Analyze and plan for anticipated changes in data capacity requirements.

Develop and implement data mining and data warehousing programs.

Develop data standards, policies, and procedures.

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

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

Provide recommendations on new database technologies and architectures.

Knowledge of interpreted and compiled computer languages.

Knowledge of secure coding techniques.

Knowledge of database theory.

Skill in using binary analysis tools (e.g., Hexedit, command code xxd, hexdump).

Skill in one way hash functions (e.g., Secure Hash Algorithm [SHA], Message Digest Algorithm [MD5]).

Skill in reading Hexadecimal data.

Skill in identifying common encoding techniques (e.g., Exclusive Disjunction [XOR], American Standard Code for Information Interchange [ASCII], Unicode, Base64, Uuencode, Uniform Resource Locator [URL] encode).

Knowledge of advanced data remediation security features in databases.

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

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

Analyze data sources to provide actionable recommendations.

Assess the validity of source data and subsequent findings.

Collect metrics and trending data.

Conduct hypothesis testing using statistical processes.

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

Develop and facilitate data-gathering methods.

Develop strategic insights from large data sets.

Present technical information to technical and non-technical audiences.

Present data in creative formats.

Program custom algorithms.

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

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

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

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

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).

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

Knowledge of applications that can log errors, exceptions, and application faults and logging.

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

Knowledge of machine learning theory and principles.

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 assessing the predictive power and subsequent generalizability of a model.

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

Skill in identifying hidden patterns or relationships.

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

Skill in performing sensitivity analysis.

Skill in developing machine understandable semantic ontologies.

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

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

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).

Skill in using data mapping tools.

Skill in using outlier identification and removal techniques.

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

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

Utilize opens source language such as R 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).