Research Analyst/Program Evaluation Lead

LMI•Tysons, VA

2d•$165,000 - $200,000•Hybrid

About The Position

LMI is seeking a Research Analyst/Program Evaluation Lead to strengthen evidence and decision quality across Department of Veterans Affairs (VA) modernization initiatives. The role establishes evaluation standards, baseline and comparison methods, evidence expectations, and interpretation approaches for operational, clinical, adoption, workforce, and technology outcomes. This position follows a hybrid work model, with an expectation of approximately 25% onsite presence at LMI’s Tysons headquarters or Washington, DC. The Research Analyst / Program Evaluation Lead ensures that program decisions are based on evidence that is methodologically sound and proportionate to the question being asked. The position must distinguish activity from outcome, correlation from causation, and statistically interesting findings from results that are operationally meaningful. The ideal candidate is both rigorous and pragmatic. The lead must be able to design credible approaches within the realities of federal data access, implementation timelines, changing operational conditions, and imperfect comparison groups, while explaining limitations in language that senior decision-makers can use. The lead should be able to establish a consistent evidence strategy across a portfolio while still tailoring methods to individual questions. Strong candidates will know which measures should be standardized for comparability, which require local context, and when the available evidence is too weak to support a confident scaling or investment decision. The ideal candidate is rigorous but practical: able to challenge unsupported impact claims, integrate quantitative and qualitative evidence, and turn methodological nuance into clear recommendations for senior decision-makers.

Requirements

Bachelor's degree in operations research, statistics, economics, public policy, public health, evaluation, applied mathematics, or a related quantitative field.
10+ years in program evaluation, operations research, applied statistics, performance measurement, health services research, or related work.
Demonstrated experience designing baselines, comparison methods, measurement frameworks, and defensible evidence standards.
Strong command of uncertainty, bias, confounding, causal limitations, mixed methods, and executive interpretation.
Recommended certification: PMP, Lean Six Sigma Green Belt, or comparable evaluation/analytics credential; equivalent advanced training is acceptable.
Ability to satisfy VA personnel vetting and applicable security, privacy, records, and data-handling requirements.
Deep practical knowledge of quasi-experimental design, observational analysis, before/after evaluation, mixed methods, performance measurement, and appropriate interpretation of non-randomized evidence.
Demonstrated ability to distinguish attribution, contribution, association, projected benefit, and observed outcome and to communicate those distinctions to non-technical leaders.
Excellent technical writing and briefing skills for producing concise evaluation plans, evidence summaries, limitations, and recommendations that support investment or implementation decisions.
Working proficiency with analytical tools and data structures sufficient to review code, calculations, data-quality findings, model outputs, and statistical results rather than relying solely on narrative summaries.
Demonstrated ability to advise senior leaders when evidence is incomplete or conflicting and to frame reasonable options for additional measurement, retesting, or decision-making under uncertainty.

Nice To Haves

12+ years evaluating federal, healthcare, technology, workforce, or operational transformation programs.
Master's or doctoral degree in operations research, statistics, economics, evaluation, public health, or a related field.
Hands-on proficiency with Python, R, SAS, SQL, or comparable analytical tools and experience with technology-pilot evaluation.
Additional certification in Lean Six Sigma Black Belt, PMI-PBA, analytics, or change/evaluation methods is preferred.
Experience evaluating healthcare, workforce, digital, operational, or technology-enabled interventions in complex real-world environments with changing implementation conditions.
Experience with federal performance management, implementation science, health services research, cost-effectiveness, or decision analysis is advantageous.
Experience developing enterprise evaluation frameworks, evidence standards, or measurement playbooks that have been reused across multiple programs or initiatives.
Published research, formal evaluation training, advanced quantitative coursework, or recognized expertise in a relevant methodological field is advantageous but not required.

Responsibilities

Define evaluation frameworks, baselines, comparison approaches, evidence thresholds, and decision criteria.
Review measurement plans for data fitness, denominators, feasibility, bias, confounding, and methodological soundness.
Distinguish observed results from projected benefits and communicate uncertainty without overstating causal claims.
Design before/after, quasi-experimental, mixed-methods, and other fit-for-purpose evaluation approaches.
Integrate operational, clinical, adoption, workforce, technical, and user-experience measures into coherent evidence.
Partner with data staff on analytical execution while retaining responsibility for evaluation rigor and interpretation.
Translate findings into decision-ready options for continue, adjust, retest, scale, defer, or stop decisions.
Create reusable evaluation templates, standards, and review criteria for future VA work.
Frame evaluation questions, logic models, outcome hypotheses, measures, data requirements, comparison strategies, and interpretation rules before implementation whenever feasible.
Review sampling approaches, denominator logic, missingness, confounding, selection effects, measurement bias, and other threats to validity that could materially change conclusions.
Develop fit-for-purpose data-collection and mixed-methods plans that connect quantitative results with user, implementation, operational, and contextual evidence.
Conduct or direct sensitivity analyses and alternative interpretations when evidence is uncertain, and clearly state what the available data can and cannot support.
Establish evaluation review checkpoints and reusable standards for measure definitions, analysis plans, evidence strength, limitations, and decision criteria across concurrent initiatives.
Coach analysts and workstream leads on evaluation thinking so outcome claims, dashboards, reports, and executive briefings use consistent terminology and do not overstate what the data supports.