The 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. Intermediate level personnel will have 4 to 6 years of experience and the equivalent of a BSBA in a cyber or data-related field. Direct experience or certifications may substitute for the academic credentials. ship is required, and the pass the background investigation for a Public Trust position. Responsibilities 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. 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. Present data in creative formats consumable by technical and nontechnical audiences. Program custom algorithms. 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 fetchingprocessing remote data). 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. Manage the compilation, cataloging, caching, distribution, and retrieval of data. Skills Assessing the predictive power and subsequent generalizability of a model. Creating and utilizing mathematical or statistical models. Data mining techniques (e.g., searching file systems) and analysis. Data pre-processing (e.g., imputation, dimensionality reduction, normalization, transformation, extraction, filtering, smoothing). Developing data dictionaries and models. Developing machine understandable semantic ontologies. Identifying hidden patterns or relationships. Performing sensitivity analysis. Regression Analysis (e.g., Hierarchical Stepwise, Generalized Linear Model, Ordinary Least Squares, Tree-Based Methods, Logistic). Transformation analytics (e.g., aggregation, enrichment, processing). Using basic descriptive statistics and techniques (e.g., normality, model distribution, scatter plots). Using data analysis tools (e.g., Excel, STATA SAS, SPSS) and mapping tools. Using outlier identification and removal techniques. Writing scripts using R, Python, PIG, HIVE, SQL, etc. Skill to identify sources, characteristics, and uses of the organizationrsquos data assets. Conducting queries and developing algorithms to analyze data structures. Generating queries and reports. Abilities Build complex data structures. Dissect a problem and examine interrelationships between data that may appear unrelated. Use data visualization tools (e.g., Flare, HighCharts, AmCharts, D3.js httpd3.js , Processing, Google Visualization API, Tableau, Raphael.js httpraphael.js ). Work from home available!