Data Scientist - Tech focus - Top Secret required to apply - DC area

Job Description

Job Description

Conducts data analytics, data engineering, data mining, exploratory analysis, predictiveanalysis, and statistical analysis, and uses scientific techniques to correlate data into graphical,written, visual and verbal narrative products, enabling more informed analytic decisions.Proactively retrieves information from various sources, analyzes it for better understanding aboutthe data set, and builds AI tools that automate certain processes. Duties typically include:creating various ML-based tools or processes, such as recommendation engines or automatedlead scoring systems. Performs statistical analysis, applies data mining techniques, and buildshigh quality prediction systems. Should be skilled in data visualization and use of graphicalapplications, including Microsoft Office (Power BI) and Tableau; major data science languages,such as R and Python; managing and merging of disparate data sources, preferably through R,Python, or SQL; statistical analysis; and data mining algorithms. Should have prior experiencewith large data Multi-INT analytics, ML, and automated predictive analytics. Contractor shall:• Create data packages, in the form of databases, reports, and visualization'• Communicate ongoing data science activities, technical findings, and data products for bothtechnical and non-technical customers• Extract relevant features from large data stores containing open source, PIA, and CAI,containing bad records, partial records, errors, or other forms of "noising."• Extract features from open source information stored in a wide range of possible formats,including JSON, XML, raw text logs, industry-specific encodings, and graph link data;• Apply natural language processing, computer vision, signal processing, and speaker and speechrecognition algorithms to identify objects in text, image, video, and audio files;• Apply descriptive and inferential statistics to describe data and makepredictions about the data, including statistical tests to determine confidence for a hypothesis,common summary statistics (e.g. mean, variance, and counts), fit distributions to datasets anduse those distributions to predict event likelihoods;• Be able to execute data science method using parallel computingframeworks (e.g. deepleaming4j, Torch, Tensor Flow, Caffe, Neon, NVIOFFICE CUDA DeepNeural Network library (cuDNN), and OpenCV)) and distributed data processing frameworks( e.g. Hadoop (including HDFS, Hbase, Hive, Impala, Giraph, Sqoop ), Spark (inlcuding MLib,GraphX, SQL and Dataframes)• Be able to execute data science method using common programming/scriptinglanguages: Python, Java, Scala, R (statistics).