Senior Data Analyst

• Data Analyst: Examines data from multiple disparate sources with the goal of providing security and privacy insight. Designs and implements custom algorithms, workflow processes, and layouts for complex, enterprise-scale data sets used for modeling, data mining, and research purposes.

• Analyze and define data requirements and specifications.
• 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 and implement data mining and data warehousing programs.
• Develop data standards, policies, and procedures.
• Develop strategic insights from large data sets.
• Effectively allocate storage capacity in the design of data management systems.
• Present data in creative formats.
• Present technical information to technical and nontechnical audiences.
• Program custom algorithms.
• Provide a managed flow of relevant information (via web-based portals or other means) based on mission requirements.
• Provide actionable recommendations to critical stakeholders based on data analysis and findings.
• Read, interpret, write, modify, and execute simple scripts (e.g., Perl, VBScript) 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).
• Utilize different programming languages to write code, open files, read files, and write output to different files.
• Utilize open 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).
• Utilize technical documentation or resources to implement a new mathematical, data science, or computer science method.
• Analyze and plan for anticipated changes in data capacity requirements.
• Manage the compilation, cataloging, caching, distribution, and retrieval of data.
• Provide recommendations on new database technologies and architectures.

Qualifications and Skills

• Skill in assessing the predictive power and subsequent generalizability of a model.
• Skill in creating and utilizing mathematical or statistical models.
• Skill in data mining techniques (e.g., searching file systems) and analysis.
• Skill in data pre-processing (e.g., imputation, dimensionality reduction, normalization, transformation, extraction, filtering, smoothing).
• Skill in developing data dictionaries.
• Skill in developing data models.
• Skill in developing machine understandable semantic ontologies.
• 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).
• Skill in identifying hidden patterns or relationships.
• Skill in one-way hash functions (e.g., Secure Hash Algorithm [SHA], Message Digest Algorithm [MD5]).
• Skill in performing format conversions to create a standard representation of the data.
• Skill in performing sensitivity analysis.
• Skill in reading Hexadecimal data.
• Skill in Regression Analysis (e.g., Hierarchical Stepwise, Generalized Linear Model, Ordinary Least Squares, Tree-Based Methods, Logistic).
• Skill in the use of design modeling (e.g., unified modeling language).
• 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 binary analysis tools (e.g., Hexedit, command code xxd, hexdump).
• 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.
• Skill to identify sources, characteristics, and uses of the organization's data assets.
• Skill in conducting queries and developing algorithms to analyze data structures.
• Skill in generating queries and reports.
• Skill in writing code in a currently supported programming language (e.g., Java, C++).

• Knowledge of advanced data remediation security features in databases.
• Knowledge of applications that can log errors, exceptions, and application faults and logging.
• Knowledge of command-line tools (e.g., mkdir, mv, ls, passwd, grep).
• Knowledge of computer algorithms.
• Knowledge of computer programming principles
• Knowledge of data administration and data standardization policies.
• Knowledge of data mining and data warehousing principles.
• Knowledge of database access application programming interfaces (e.g., Java Database Connectivity [JDBC]).
• Knowledge of database management systems, query languages, table relationships, and views.
• Knowledge of database theory.
• Knowledge of digital rights management.
• Knowledge of enterprise messaging systems and associated software.
• Knowledge of how to utilize Hadoop, Java, Python, SQL, Hive, and Pig to explore data.
• Knowledge of Information Theory (e.g., source coding, channel coding, algorithm complexity theory, and data compression).
• Knowledge of interpreted and compiled computer languages.
• Knowledge of low-level computer languages (e.g., assembly languages).
• Knowledge of machine learning theory and principles.
• Knowledge of mathematics (e.g. logarithms, trigonometry, linear algebra, calculus, statistics, and operational analysis).
• 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 secure coding techniques.
• 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).
• Knowledge of network access, identity, and access management (e.g., public key infrastructure, Oauth, OpenID, SAML, SPML).
• Knowledge of operating systems.
• Knowledge of computer networking concepts and protocols, and network security methodologies.
• Knowledge of cyber threats and vulnerabilities.
• Knowledge of cybersecurity and privacy principles.
• Knowledge of laws, regulations, policies, and ethics as they relate to cybersecurity and privacy.
• Knowledge of risk management processes (e.g., methods for assessing and mitigating risk).
• Knowledge of specific operational impacts of cybersecurity lapses.

Senior level positions require seven(7) + years of relevant cyber-security experience and an advanced degree in a technical/cyber-related field. Direct experience or directly relevant certifications may substitute for the academic credentials

• Ability to accurately and completely source all data used in intelligence, assessment and/or planning products.
• 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).

Location: Must reside within the DC Metro area. Remote and Contractor Site 1515 Wilson Blvd. Arlington, VA 22209