Data Scientist/Data Science Lead

LMI is seeking a Data Scientist/Data Science Lead to deliver hands-on analytical leadership for VA modernization initiatives. The role blends advanced analytics, machine learning, data-quality rigor, and technical review to support complex mission and operational decisions, with coaching and standards-setting for a broader team.

Location and Work Model

• Tysons, VA (hybrid)
• US-DC, Washington, DC
• Onsite presence: Approximately 25% at LMI’s Tysons headquarters or Washington, DC

Compensation

• Annual salary: USD 110,000 - 145,000

Key Responsibilities

• Lead analytical strategy for complex operational, clinical, workflow, and technology questions.
• Evaluate data readiness and fitness for intended measures, including data quality, sensitivity, lineage, authoritative sources, and measurement suitability.
• Develop and review statistical models, machine-learning methods, exploratory analyses, and AI/LLM evaluations.
• Create and review reporting-ready data models, transformations, validation logic, and analytical pipelines.
• Conduct 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.
• Set analytical standards covering reproducibility, peer review, validation, documentation, version control, data-quality checks, and transparent communication of assumptions and limitations.
• Lead advanced analytical and machine-learning work across feature definition, model selection, validation strategy, error analysis, sensitivity testing, and bias or fairness assessment, including interpretation.
• Create reusable notebooks, code patterns, analytical templates, and quality checks to improve consistency and accelerate future delivery.
• Coordinate with engineering and platform resources on analytical or model handoff, including data pipelines, monitoring requirements, performance expectations, and maintainability considerations.
• Lead technical reviews of analytical plans and results to ensure alignment between method, data, validation, assumptions, and the decision needs.
• Work with data owners and governance stakeholders to strengthen data definitions, provenance, access patterns, quality expectations, and responsible use when recurring analytics reveal systemic issues.

Required Qualifications

• Education: Bachelor’s degree in a related quantitative field (data science, statistics, mathematics, computer science, operations research, economics, engineering, public health, or a related quantitative field).
• Experience: 7+ years in data science, advanced analytics, machine learning, statistical analysis, or related work.
• Strong Python and SQL skills, including experience with statistical modeling, validation, and complex-data interpretation.
• Experience translating ambiguous operational, business, 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 a comparable provider.
• Ability to satisfy VA personnel vetting and requirements for sensitive or regulated data.
• Strong statistical foundation including 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, including diagnosing data quality, missingness, leakage, representativeness, and lineage concerns prior to modeling.
• Technical leadership experience reviewing others’ analytical work, mentoring staff, setting coding and validation standards, and explaining complex findings to senior non-technical stakeholders.
• Ability to own 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.

Technologies

• Python
• SQL
• Microsoft/Azure
• AWS
• Google Cloud
• Databricks
• AI
• LLM
• NLP
• Power BI

Desired Qualifications

• 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.
• Preference for experience operationalizing analytics or machine-learning work through repeatable pipelines, model monitoring, MLOps, or collaboration with production engineering teams.
• Advanced certifications in Azure/AWS/Google machine learning, Databricks, data science, or analytics are valuable when paired with demonstrated statistical depth.
• Preferred experience in healthcare, federal health, clinical analytics, operations research, or other domains where data quality and context affect decision interpretation.
• Evidence of technical leadership through publications, conference presentations, internal standards, mentoring, peer review, or leadership of complex analytical work.