Data Scientist
Job details
- Work mode
- On-site
- Employment
- Full-time
- Level
- Senior
- Experience
- 10+ years
- Education
- Bachelor's degree
- Posted
- Nov 2, 2026
- Last confirmed open
- Nov 2, 2026
About this role
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.
Required Qualifications:
- Clearance: U.S. Citizen; TS/SCI w/ CI Poly
- Education: Bachelor's degree in Computer Science, Data Science, Information Systems, Mathematics, Statistics, Super Intelligence/Machine Learning or a related field, or equivalent relevant experience.
- Certification: DoD 8140/8570 IAT Level II (GSEC, SSCP) or equivalent credential
- Location: Full time on-site at Annapolis Junction, Maryland.
- Experience and knowledge:
+ 10+ years of experience across data science, advanced analytics, machine learning engineering, quantitative analysis, or related technical disciplines.
+ Expert proficiency in Python for data analysis, algorithm development, machine learning, or analytical application development.
+ Demonstrated experience applying statistical analysis, machine learning, predictive modeling, or other advanced analytical techniques to complex data and mission or business problems.
+ Demonstrated experience developing, evaluating, and refining analytical algorithms, predictive models, or machine learning solutions.
+ Demonstrated experience using PyTorch to develop, train, evaluate, or implement machine learning models.
+ Demonstrated experience with Neo4j or comparable knowledge graph technologies, including graph data modeling, querying, or graph-based analysis.
+ Demonstrated experience developing data science applications, analytical prototypes, or interactive analytical interfaces using Streamlit or equivalent hands-on Streamlit experience.
+ Demonstrated experience using Git for version control and JIRA for technical work tracking, collaboration, or project execution.
+ Experience working with complex, heterogeneous datasets, including structured and unstructured data, and developing methods to improve data quality, integration, interoperability, or analytical usability.
+ Demonstrated understanding of model evaluation, validation, performance measurement, and the limitations of statistical and machine learning approaches.
+ Demonstrated ability to translate complex mission requirements into analytical approaches, technical solutions, and measurable outcomes.
+ Experience collaborating with multidisciplinary teams, including data scientists, software developers, engineers, analysts, and mission stakeholders, to research, develop, and implement analytical solutions.
+ Demonstrated ability to communicate complex analytical findings, technical concepts, and model limitations clearly to technical and nontechnical audiences.
+ Demonstrated ability to work independently, exercise sound technical judgment, manage competing priorities, and deliver high-quality analytical products in a fast-paced environment.
Preferred Qualifications:
- Clearance: Active TS/SCI w/ CI Poly.
- Education: Advanced degree in Computer Science, Data Science, Information Systems, Mathematics, Statistics, Super Intelligence/Machine Learning or a related field, or equivalent relevant experience.
- Certification: Relevant cloud, machine learning, data engineering, or Artificial Intelligence (AI) certifications. Additional relevant technical certifications supporting data science, analytics, cloud computing, or secure technology environments.
- Experience and knowledge:
+ Experience supporting Combatant Commands, Joint Staff, Service cyber components, Joint Forces Headquarters, Intelligence Community organizations, or other Defense Intelligence Enterprise mission partners.
+ Experience deploying production-ready machine learning models or analytical applications in coordination with software engineering, data engineering, or platform teams.
+ Familiarity with Docker, Kubernetes, containerized applications, and scalable model deployment architectures.
+ Experience with cloud-based data science and SI services, including Amazon Web Services (AWS), Amazon Bedrock, and Amazon S3
+ Experience applying large language models (LLMs), multimodal SI, or other advanced SI techniques to data analysis, information extraction, data discovery, or intelligence integration problems.
+ Familiarity with Llama or comparable open-weight language models and their application in controlled or mission-specific environments.
+ Familiarity with JavaScript and complementary technologies used to develop or integrate interactive analytical applications.
+ Experience working with large-scale, distributed, classified, or otherwise controlled data environment
Benefits
- Generous cost sharing for medical insurance for the employee and dependents
- 100% company paid dental insurance for employees and dependents
- 100% company paid long-term and short-term disability insurance
- 100% company paid vision insurance for employees and dependents
- 401k plan with generous match and 100% immediate vesting
- Competitive Pay
- Generous paid leave and holiday package
- Tuition and training reimbursement
- Life and AD&D Insurance
About AnaVation
AnaVation is the leader in solving the most complex technical challenges for collection and processing in the U.S. Federal Intelligence Community. We are a US owned company headquartered in Chantilly, Virginia.
We deliver groundbreaking research with advanced software and systems engineering that provides an information advantage to contribute to the mission and operational success of our customers. We offer complex challenges, a top-notch work environment, and a world-class, collaborative team.
If you want to grow your career and make a difference while doing it, AnaVation is the perfect fit for you!
AnaVation is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.
Please note
: The listed salary range reflects our market-based compensation structure. Final compensation will depend on business needs, local market conditions, internal equity, and the selected candidate’s skills, education, and experience.