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 u