Director, Data Science

Fidelity Investments • Boston, MA

6d

About The Position

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 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.

Requirements

• 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)
• 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
• 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 analysis while maintaining correctness, reproducibility, and scientific rigor.
• Thinks like a scientist: hypothesis‑first, evidence‑driven, and principled
• High bar for rigor, interpretability, and defensibility of results
• Comfortable challenging senior stakeholders using data and logic
• Values clarity, elegance, and correctness over technical novelty
• Operates as a trusted expert rather than a delivery engineer

Responsibilities

• 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
• 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
• 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?”
• Design statistically grounded, interpretable segmentations with clear hypotheses and stability checks
• Develop probabilistic and causal models to inform prioritization and intervention strategies
• Guide recommendation logic rooted in statistics, behavioral science, and optimization—not black‑box ML
• Analyze longitudinal behavior patterns to identify drivers, frictions, and causal levers
• Apply time‑series and probabilistic forecasting with uncertainty and scenario analysis
• 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.
• 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
• 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

Benefits

• comprehensive health care coverage
• emotional well-being support
• market-leading retirement
• generous paid time off
• parental leave
• charitable giving employee match program
• educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career

What This Job Offers

• Job Type: Full-time
• Career Level: Senior