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Data Scientist - Chantilly VA

Chantilly, VA · Computer/Software

Day to day responsibilities include:

• The Contractor shall perform end-to-end quality assurance of data feeds and data sets.
• The Contractor shall provide support for data triage and assessment at the Sponsor’s site.
• The Contractor shall identify and document areas for improvement in workflows or systems.
• The Contractor shall attend regular stand-up meetings.
• The Contractor shall provide input to code reviews.
• The Contractor shall cross-train on existing collection tools.
• The Contractor shall support monitoring, alerting, and reporting out (e.g. dashboards).
• The Contractor shall support new use cases.
• The Contractor shall research and document options for collecting or aggregating data from a variety of web based and internal Sponsor platforms.
• The Contractor shall evaluate web based platforms’ ability to detect or deny access.
• The Contractor shall make recommendations on approaches to acquire information.
• The Contractor shall use appropriate tools and computer programming languages, such as Python scripts, to collect and process data from a variety of sources.
• The Contractor shall use Sponsor-network APIs to programmatically access data.
• The Contractor shall create, maintain, and enhance systems in support of data exploitation.
• The Contractor shall create or improve custom collection scripts written in Python.
• The Contractor shall create or improve scripts leveraging APIs for collection needs.
• The Contractor shall automate data clean-up and conditioning of collected data.
• The Contractor shall automate data management and dissemination steps.


• Demonstrated experience programming in Python.
• Demonstrated experience analyzing questions, formulating requirements, determining suitable analytic approaches, evaluating results, and communicating findings to partners and stakeholders.
• Demonstrated experience working with data in a variety of structured and unstructured formats.
• Demonstrated experience with a variety of database tools, such as SQL and Presto, and data lakes/S3 data.
• Demonstrated experience with data visualization tools, such as notebook-based visualization libraries, especially Elasticsearch, Kibana, and Tableau.
• Demonstrated ability to translate complex, technical findings into an easily understood narrative in graphical, verbal, or written form.
• Demonstrated experience with AI/ML, such as natural language processing in a production environment.


• Demonstrated experience programming in common compiled or interpreted languages, such as Python and R.
• Demonstrated experience with data management tools, such as Hadoop, MapReduce, or similar.
• Demonstrated experience with technical operations.
• Demonstrated experience with technical targeting.
• Demonstrated experience conducting data science using the Apache Zeppelin and Jupyter notebooks platforms and Spark/Pyspark.
• Demonstrated experience collaborating with colleagues to develop customer-tailored products.


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