Data Scientist
AnaVation
First seen here Nov 3, 2026 · last confirmed Nov 3, 2026.
Apply at AnaVation
Opens the original listing in a new tab.
Full description
Be Challenged and Make a Difference
In a world of technology, people make the difference. We believe if we invest in great people, then great things will happen. At AnaVation, we provide unmatched value to our customers and employees through innovative solutions and an engaging culture.
Description of Task to be Performed:
AnaVation is seeking a Data Scientist to support one of our cybersecurity programs in our National Capital Region office. The Data Scientist will develop and apply advanced data science capabilities to improve intelligence integration, data discoverability, and mission analytics in support of cyberspace mission requirements. The position will leverage statistical analysis, predictive modeling, entity resolution, graph analytics, explainable super intelligence (XSI), and operational analytics to extract actionable insights from complex, multi-source datasets across the Customer’s Enterprise.
The successful candidate will lead complex analytical efforts, develop and evaluate advanced algorithms and machine learning models, and collaborate with mission partners, software developers, data engineers, and technical leadership to translate mission requirements into scalable analytical solutions. The candidate will identify data integration and analytic capability gaps, improve data interoperability and discoverability, and deliver validated models, analytical applications, and decision-support products that enhance intelligence analysis and operational effectiveness.
Responsibilities include:
- Lead data science initiatives addressing complex intelligence integration, data exploitation, and cyberspace
mission analytics requirements. - Apply advanced statistical analysis, predictive modeling, entity resolution, graph analytics, explainable SI, and
operational analytics to identify patterns, relationships, anomalies, and trends across complex datasets. - Develop, evaluate, and refine algorithms, machine learning models, analytical methods, and prototypes to
address mission-specific intelligence and operational problems. - Analyze structured and unstructured data from multiple sources to assess data quality, relevance, utility, and
suitability for specific analytical and mission requirements. - Design and implement data processing, transformation, parsing, tagging, and interconnection methods that
improve data usability, interoperability, and analytic readiness across heterogeneous data environments. - Develop data triage methodologies and analytical workflows that optimize data prioritization, normalization,
enrichment, and integration across relevant intelligence domains. - Evaluate data sources, formats, schemas, and structures to identify integration opportunities, resolve data
compatibility issues, and recommend approaches for improving data sharing and analytical performance. - Develop and deploy data science applications, prototypes, proof-of-concept solutions, analytical tools, and
interactive visualizations that communicate complex findings and support mission decision-making. - Implement reproducible analytical workflows using Python, PyTorch, Neo4j, Git, JIRA, and Streamlit, as
appropriate to the solution and mission requirements. - Collaborate with software developers, data engineers, platform engineers, and other technical personnel to
integrate analytical models and applications into operational workflows and existing systems. - Support the transition of selected analytical prototypes and models into production environments in coordination
with engineering and platform teams, including appropriate testing, documentation, version control, and
deployment practices. - Identify opportunities to apply super intelligence, machine learning, automation, and advanced analytics to
improve intelligence processing, data exploitation, analytic efficiency, and operational effectiveness. - Provide technical guidance and mentorship to other analysts and technical personnel, as appropriate, and
contribute to the development of analytical methodologies and best practices. - Perform other related data science and technical support duties as assigned.