Principal Data Scientist

Job description

Lead the design and analysis of large-scale experiments and observational studies; select and defend the right causal inference approach for each problem. Develop and productionize advanced machine learning and causal models that drive strategic decisions. Establish best practices for experimentation, statistical rigor, and model validation across the team. Influence metric definitions, measurement strategy, and roadmap through data-driven insight.

Mentor Associate data scientists and review analyses for methodological soundness. Communicate complex results and trade-offs to senior stakeholders. Bachelor's Degree in Computer Science, Information Technology (IT), or related field AND 10+ years technical support, technical consulting experience, or information technology experience These requirements include but are not limited to the following specialized security screenings: Citizenship & Citizenship Verification: This position requires verification of U.S. citizenship due to citizenship-based legal restrictions.

Specifically, this position supports United States federal, state, and/or local United States government agency customer and is subject to certain citizenship-based restrictions where required or permitted by applicable law. To meet this legal requirement, citizenship will be verified via a valid passport. Bachelor's Degree in Computer Science, Information Technology, or related field AND 15+ years of technical support, technical consulting experience, or information technology experience OR equivalent experience.

Expert-level causal inference — able to choose, apply, and critique experimental and observational methods (DiD, IV, propensity/matching, uplift, synthetic control). Advanced statistics — deep command of inference, uncertainty quantification, and both Bayesian and frequentist approaches. Strong ML expertise — end-to-end modeling, robust validation, and production deployment. Experience evaluating LLMs and/or AI agents — building rigorous evaluation frameworks, benchmarking, and measuring quality, safety, and reliability at scale.

Track record leading impactful experimentation programs. Experience with causal ML libraries (DoWhy, EconML, CausalML) at scale. Demonstrated technical leadership and mentorship. Data storytelling — translating complex analyses into compelling data stories through enhanced visualizations in Python (e.g., matplotlib, seaborn, plotly) or other tools. Expert proficiency in Python or R and SQL.