Data Scientist

AnaVation•Fort Meade, MD

1d•Onsite

About The Position

AnaVation is seeking a Data Scientist to support a cybersecurity program at its National Capital Region office. The role develops and applies advanced data science capabilities to improve intelligence integration, data discoverability, and mission analytics in support of cyberspace mission requirements. It uses 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 Data Scientist leads complex analytical efforts, develops and evaluates advanced algorithms and machine learning models, and works with mission partners, software developers, data engineers, and technical leadership to turn mission requirements into scalable analytical solutions. The role identifies data integration and analytic capability gaps, improves interoperability and discoverability, and delivers validated models, analytical applications, and decision-support products that support intelligence analysis and operational effectiveness. The position is full-time and on-site in Annapolis Junction, Maryland.

Requirements

U.S. Citizen with TS/SCI clearance and CI Poly.
Bachelor’s degree in Computer Science, Data Science, Information Systems, Mathematics, Statistics, Super Intelligence/Machine Learning, or a related field, or equivalent relevant experience.
DoD 8140/8570 IAT Level II certification (GSEC, SSCP) or equivalent credential.
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.
Experience applying statistical analysis, machine learning, predictive modeling, or other advanced analytical techniques to complex data and mission or business problems.
Experience developing, evaluating, and refining analytical algorithms, predictive models, or machine learning solutions.
Experience using PyTorch to develop, train, evaluate, or implement machine learning models.
Experience with Neo4j or comparable knowledge graph technologies, including graph data modeling, querying, or graph-based analysis.
Experience developing data science applications, analytical prototypes, or interactive analytical interfaces using Streamlit or equivalent hands-on Streamlit experience.
Experience using Git for version control and JIRA for technical work tracking, collaboration, or project execution.
Experience with complex, heterogeneous datasets, including structured and unstructured data, and developing methods to improve data quality, integration, interoperability, or analytical usability.
Understanding of model evaluation, validation, performance measurement, and limitations of statistical and machine learning approaches.
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.
Ability to communicate complex analytical findings, technical concepts, and model limitations clearly to technical and nontechnical audiences.
Ability to work independently, exercise sound technical judgment, manage competing priorities, and deliver high-quality analytical products in a fast-paced environment.

Nice To Haves

Active TS/SCI clearance with CI Poly.
Advanced degree in Computer Science, Data Science, Information Systems, Mathematics, Statistics, Super Intelligence/Machine Learning, or a related field, or equivalent relevant experience.
Relevant cloud, machine learning, data engineering, or Artificial Intelligence certifications.
Additional technical certifications supporting data science, analytics, cloud computing, or secure technology environments.
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 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 environments.

Responsibilities

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 for mission-specific intelligence and operational problems.
Analyze structured and unstructured data from multiple sources to assess data quality, relevance, utility, and suitability for analytical and mission requirements.
Design and implement data processing, transformation, parsing, tagging, and interconnection methods to improve data usability, interoperability, and analytic readiness across heterogeneous data environments.
Develop data triage methodologies and analytical workflows for data prioritization, normalization, enrichment, and integration across relevant intelligence domains.
Evaluate data sources, formats, schemas, and structures to identify integration opportunities, resolve compatibility issues, and recommend ways to improve data sharing and analytical performance.
Develop and deploy data science applications, prototypes, proof-of-concept solutions, analytical tools, and interactive visualizations that communicate findings and support mission decisions.
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 transition of selected analytical prototypes and models into production environments with engineering and platform teams, including testing, documentation, version control, and deployment practices.
Identify opportunities to use 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 analysts and technical personnel, as appropriate, and contribute to analytical methodologies and best practices.
Perform other related data science and technical support duties as assigned.