Job Description
VP, Data Science – Quantitative Research, Measurement & Strategy
Are you interested in leading the scientific backbone of a modern AI organization where rigor, measurement, and evidence drive strategy and execution? Fidelity Institutional is seeking a VP, Data Science to lead its Quantitative Research & Measurement function within the AI Center of Excellence (AI CoE).
The VP of Data Science is accountable for how Fidelity Institutional measures impact, establishes truth, runs experiments, proves causality, and optimizes decisions at scale. This role requires deep, hands‑on proficiency with large language models, generative AI, and agentic systems, while ensuring their application is scientifically sound, empirically validated, and grounded in rigorous quantitative evidence. The VP of Data Science is accountable for ensuring insights derived from both traditional modeling and GenAI techniques are defensible, measurable, and decision‑relevant.
This is a hands-on leadership role that sets the vision for advanced analytics as a center of excellence for measurement, experimentation, and quantitative decision science, partnering closely with Platform, Product, BI, Risk, and Business leaders.
The Team
The Data Science organization within the FI AI CoE serves as the quantitative authority for the Fidelity Institutional. This team includes senior statisticians, quantitative researchers, optimization experts, and advanced data scientists who tackle the most analytically complex questions facing Fidelity Institutional.
Under This VP’s Leadership, The Team Operates As
- Owners of measurement and evaluation frameworks
- Experts in experimentation, causal inference, and incrementality
- Stewards of advanced quantitative modeling and optimization
- Trusted advisors on whether initiatives actually worked and why
Key Responsibilities
Measurement & Decision Science Strategy
- Define and own the vision for measurement, experimentation, and quantitative decision‑making
- Establish standards for what should be measured and how impact should be proven across FI initiatives
- Ensure consistent, defensible evaluation methodologies across analytics, AI, and business programs
- Elevate data science from prediction to decision quality
Experimentation & Statistical Governance
- Set strategy and standards for experimental design across the organization
- Ensure statistical rigor in A/B testing, quasi‑experiments, and observational studies
- Define best practices for power analysis, bias control, inference, and interpretation
- Act as executive sponsor for experimentation platforms and methodologies
Causal Inference & Incrementality Leadership
- Own the FI approach to causal inference, attribution, and incrementality measurement
- Ensure leaders can distinguish correlation from causation in decision‑making
- Sponsor advanced causal techniques such as Difference‑in‑Differences, synthetic controls, and uplift modeling
- Provide executive guidance on “Did it actually work?” questions
Optimization & Quantitative Modeling
- Establish optimization and decision‑science capabilities across FI
- Guide formulation of objective functions, constraints, and trade‑offs aligned to business goals
- Oversee deployment of optimization methods for prioritization, planning, and resource allocation
- Ensure optimization outputs are interpretable and actionable
Quantitative Research Leadership
- Set direction for hypothesis‑driven research to answer strategic business questions
- Sponsor development of advanced statistical, econometric, and ML models where appropriate
- Ensure models are theoretically sound, well‑documented, and fit‑for‑purpose
- Promote scientific integrity and intellectual rigor across the AI CoE
Forecasting & Planning Analytics
- Lead forecasting using time‑series and probabilistic techniques
- Ensure uncertainty and scenario analysis are incorporated into forecasts
- Partner with business leaders to integrate forecasts into planning and decision cycles
Advanced Analytics Domains
- Recommendation Systems: Lead recommendation approaches rooted in statistical learning, optimization, and behavioral science. Ensure recommendation logic is explainable, empirically validated, and optimized against business and client outcomes rather than treated as black‑box ML.
- Segmentation & Clustering: Lead the design and evaluation of statistically grounded segmentation frameworks to uncover meaningful heterogeneity in clients, advisors, firms, and institutional behaviors. Ensure segmentations are interpretable, stable, and actionable, with clear hypotheses for how segments drive differentiated strategy and outcomes.
- Propensity, Likelihood, and Uplift Modeling: Develop and govern probabilistic and causal models that estimate likelihood of action and incremental impact of interventions. Own rigor around bias control, validation, and lift measurement to ensure models support decision‑making through incrementality.
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