Data Science Engineer Jobs in Virginia

The Data Scientist / Data Science Lead is expected to be a working technical leader, not solely a reviewer. The person should be able to inspect raw data, write code, test assumptions, choose appropriate methods, and step into difficult analytical problems while also setting standards that improve the quality and reproducibility of the broader team.

The strongest candidate will combine statistical judgment with practical mission orientation. This means understanding when sophisticated methods are justified, when simpler approaches are more defensible, how data limitations affect conclusions, and how to connect analytical results to operational or clinical decisions.

The lead will also be responsible for raising the analytical capability of the team. This includes setting expectations for peer review, helping staff choose appropriate methods, identifying when a question requires deeper expertise, and creating a culture where limitations and negative findings are reported as clearly as positive results.

The ideal candidate combines strong statistical coding with practical data-quality rigor and can explain evidence, uncertainty, bias, missingness, and limitations to technical, operational, clinical, and executive audiences.

• Lead analytical strategy for complex operational, clinical, workflow, and technology questions.
• Assess data readiness, quality, sensitivity, lineage, authoritative sources, and fitness for intended measures.
• Develop and review statistical models, machine-learning methods, exploratory analyses, and AI/LLM evaluations.
• Design reporting-ready data models, transformations, validation logic, and analytical pipelines.
• Perform hands-on Python/SQL analysis and troubleshoot difficult data or modeling issues.
• Partner with clinical and evaluation staff on baselines, denominators, comparisons, outcomes, and evidence limitations.
• Communicate uncertainty, data-quality issues, bias, and causal limitations clearly to decision-makers.
• Coach analysts and establish reusable analytics, validation, and peer-review practices.
• Establish analytical standards for reproducibility, peer review, validation, documentation, version control, data-quality checks, and transparent communication of assumptions and limitations.
• Lead feature definition, model selection, validation strategy, error analysis, sensitivity testing, bias/fairness assessment, and interpretation for advanced analytical or machine-learning work.
• Create reusable notebooks, code patterns, analytical templates, and quality checks that accelerate future work while improving consistency across analysts and data scientists.
• Coordinate with engineering and platform resources on model or analytical handoff, including data pipelines, monitoring requirements, performance expectations, and maintainability considerations.
• Lead technical reviews of analytical plans and results, ensuring the method, data, validation, assumptions, and interpretation are aligned to the decision the customer needs to make.
• Partner with data owners and governance stakeholders to improve data definitions, provenance, access patterns, quality expectations, and responsible use when recurring analytical work exposes systemic data issues.

• Bachelor's degree in data science, statistics, mathematics, computer science, operations research, economics, engineering, public health, or a related quantitative field.
• 7+ years in data science, advanced analytics, machine learning, statistical analysis, or related work.
• Strong hands-on Python and SQL skills with demonstrated statistical modeling, validation, and complex-data interpretation experience.
• Experience translating ambiguous business, operational, or clinical questions into defensible analytical methods and decision products.
• Recommended certification: cloud data science/ML, analytics, or data-platform credential from Microsoft/Azure, AWS, Google Cloud, Databricks, or comparable provider.
• Ability to satisfy VA personnel vetting and requirements for sensitive or regulated data.
• Strong statistical foundation covering regression, classification, sampling, validation, uncertainty, experimental or quasi-experimental reasoning, and appropriate interpretation of observational data.
• Demonstrated ability to work with large, messy, multi-source datasets and independently diagnose data-quality, missingness, leakage, representativeness, and lineage concerns before modeling.
• Technical leadership experience reviewing others' analytical work, mentoring staff, setting coding and validation standards, and explaining complex findings to senior non-technical stakeholders.
• Experience owning analytical work from problem formulation through data acquisition, modeling, validation, interpretation, executive communication, and transition to recurring or operational use.
• Strong written and verbal communication skills for explaining statistical concepts, uncertainty, model behavior, and data limitations without either oversimplifying or overwhelming the audience.

• 9+ years in applied data science or analytics, including federal health, healthcare, regulated data, or enterprise modernization.
• Advanced degree in statistics, data science, operations research, public health, computer science, or a related quantitative discipline.
• Experience with NLP, generative AI/LLMs, causal inference, time-series, predictive modeling, Databricks, Snowflake, or Power BI.
• Additional advanced cloud ML, Databricks, analytics, or data-engineering certification is preferred.
• Experience operationalizing analytics or machine-learning work through repeatable pipelines, model monitoring, MLOps practices, or collaboration with production engineering teams is preferred.
• Advanced certifications in Azure/AWS/Google machine learning, Databricks, data science, or analytics are valuable when paired with demonstrated statistical depth.
• Experience in healthcare, federal health, clinical analytics, operations research, or other domains where data quality and context materially affect decision interpretation is preferred.
• Evidence of technical leadership through publications, conference presentations, internal standards, mentoring, peer review, or leadership of complex analytical work can strengthen a candidate profile.