Director, Data Science

Job Description: Principal Data Scientist – Quantitative Decision Science & Advanced Analytics   Note: Fidelity will not provide immigration sponsorship for this position. Are you interested in operating as a senior scientific leader—owning   truth, rigor, and decision quality   for complex business problems? Fidelity   Institutional’s   AI Center of Excellence (AI CoE) is seeking a   Principal Data Scientist   to serve as a highly tenured individual contributor and domain authority in data science, quantitative modeling, and advanced analytics.   This role is   intentionally Data Science–first,   with emphasis on hypothesis‑driven analysis, statistical rigor, causal reasoning, and decision science. The Principal Data Scientist is accountable for   what the model means, whether it is correct, and whether it should be trusted —not for building or operating production systems.   The Team   The Data Science function within the Fidelity Institutional AI CoE operates as the authority on measurement, experimentation, and quantitative decision‑making. The team comprises senior data scientists, statisticians, and quantitative researchers who partner closely with platform, product, BI, and business teams, while maintaining clear ownership of scientific rigor, evaluation frameworks, and analytical truth.   As a Principal Data Scientist, you will operate as a   scientific owner and mentor,   influencing methodology, standards, and strategic direction across multiple initiatives.   Key Responsibilities   Advanced Data Science & Quantitative Modeling   Lead hypothesis‑driven analyses to answer high‑impact strategic and business questions   Design, develop, and evaluate statistical, econometric, and machine learning models where appropriate   Ensure models are theoretically sound, empirically validated, interpretable, and fit‑for‑purpose   Review and challenge modeling approaches for bias, stability, assumptions, and misuse   Measurement, Evaluation & Decision Science   Define how success should be measured for complex analytics and AI‑enabled initiatives   Design robust evaluation frameworks including offline validation, back‑testing, and live measurement   Ensure stakeholders can distinguish correlation from causation in analytical results   Elevate analytics from prediction accuracy   to decision quality and business impact   Experimentation & Causal Inference   Design and review experiments including A/B tests, quasi‑experiments, and observational studies   Apply causal inference techniques (e.g., uplift modeling,   DiD , matched controls) to assess incrementality   Guide best practices for power analysis, inference, and result interpretation   Serve as a subject‑matter expert on “What worked, why, and by how much?”   Advanced Analytics Domains   Segmentation & Clustering:   Design statistically grounded, interpretable segmentations with clear hypotheses and stability checks   Propensity, Likelihood & Uplift Modeling:   Develop probabilistic and causal models to inform prioritization and intervention strategies   Recommendation & Prioritization Analytics:   Guide recommendation logic rooted in statistics, behavioral science, and optimization—not   black‑box   ML   Behavioral & Journey Analytics:   Analyze longitudinal behavior patterns to identify drivers, frictions, and causal levers   Forecasting & Planning Analytics:   Apply time‑series and probabilistic forecasting with uncertainty and scenario analysis   Large Language Models & Generative AI:   Design, evaluate, and implement LLM-based solutions — including RAG pipelines, classification, and extraction tasks — with rigorous benchmarking, calibration analysis, hallucination measurement, and bias auditing to ensure outputs are explainable.   Scientific Leadership & Governance (Non‑Managerial)   Act as a senior reviewer and methodological authority across data science initiatives   Set informal standards for rigor, documentation, and reproducibility   Mentor senior and mid‑level data scientists through technical guidance and peer review   Business Partnership & Influence   Translate complex quantitative results into clear, decision‑oriented narratives for senior stakeholders   Challenge assumptions and narratives not supported by evidence   Influence strategy by grounding discussions in data, causality, and expected impact   Expertise and Skills You Bring   Education & Experience   Master’s or PhD in Statistics, Economics, Mathematics, Operations Research, Computer Science, or related quantitative discipline   10–14+ years of experience in data science, quantitative research, or advanced analytics   Proven track record of owning complex analytical problems   end‑to‑end   (from question formulation to decision impact)   Core Data Science & Scientific Expertise   Deep expertise in statistics, probability, and experimental design   Strong command of causal inference and incrementality measurement   Solid grounding in forecasting, optimization, and decision science   Demonstrated ability to assess modeling correctness, assumptions, and limitations   Technical Foundation     Advanced proficiency in Python for analysis and modeling (NumPy, Pandas, SciPy,   Statsmodels , Scikit‑learn)   Strong SQL skills and experience working with large analytical datasets (e.g.,   Snowflake)   Hands ‑ on proficiency with large language models and generative AI, including prompt design, retrieval ‑ augmented generation, structured outputs, and agentic workflows, with demonstrated rigor in designing evaluations, defining task ‑ specific metrics, and applying statistical testing to assess reliability, calibration, hallucination risk, and incremental value over non ‑ generative approaches. Equally proficient in hands ‑ on code development as well as the effective use of AI ‑ powered coding assistants, applying both to accelerate ana