Vice President, Data Science

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. 

• Behavioral & User Journey Analytics: Apply hypothesis‑driven analytics to understand longitudinal behavior, action sequences, and decision pathways across journeys, focusing on causal drivers and friction points. 

• Network & Relationship Analytics: Advance graph‑based analytics to model institutional, firm‑level, and advisor relationships with emphasis on influence, connectivity, exposure, and systemic effects supported by statistical validation. 

• Large Language Models & Generative AI: Lead hands‑on design, experimentation, and evaluation of LLM‑ and agent‑based systems for knowledge extraction, classification, summarization, reasoning, and decision support. Develop and implement task‑level evaluation frameworks, prompt and retrieval strategies, and controlled experiments to assess reliability, calibration, hallucination risk, bias, and robustness. Build and test retrieval‑augmented generation and agentic workflows with explicit hypotheses about information quality and decision impact, and quantify incremental value relative to non‑generative statistical and machine‑learning baselines. 

Organizational Leadership & Talent Development 

• Senior Data Science Leadership: Build, lead, and mentor senior data science and quantitative research leaders who operate as scientific owners of modeling, inference, and measurement. 

• Scientific Career Paths: Define clear career paths and skill expectations for scientifically‑oriented data scientists, emphasizing statistics, causal inference, experimental design, decision science, and interpretability. 

• Culture of Rigor and Learning: Foster a culture that values curiosity, peer review, principled debate, experimentation, and continuous learning in quantitative methods. 

Executive Partnership & Influence 

• Serve as a trusted advisor to senior business and technology leaders 

• Translate complex quantitative findings into clear executive narratives 

• Influence strategy by grounding discussions in evidence, causality, and expected impact 

The Expertise and Skills You Bring 

Education & Experience 

• Master’s or PhD in Statistics, Economics, Mathematics, Operations Research, Computer Science, or related quantitative discipline 

• 15+ years of experience in advanced analytics, quantitative research, or data science 

• Proven leadership of senior quantitative teams and FI‑level analytics programs 

Quantitative & Scientific Expertise 

• Deep expertise in statistics, probability, and experimental design 

• Strong background in causal inference and incremental impact measurement 

• Advanced knowledge of optimization, econometrics, and forecasting 

• Ability to assess modeling approaches for correctness, bias, and suitability 

Technical Foundation 

• Advanced proficiency in Python for statistical modeling, experimentation, simulation, and analysis (NumPy, Pandas, SciPy, Statsmodels, Scikit‑learn) 

• Strong working knowledge of SQL and large‑scale analytical datasets (e.g., Snowflake) 

• Hands‑on proficiency with large language models and generative AI, including prompt design, retrieval‑augmented generation, structured outputs, agentic workflows, and quantitative evaluation of LLM behavior using task‑specific metrics and statistical testing 

Leadership & Ways of Working 

• Thinks like a scientist and leader: hypothesis‑first, evidence‑driven, and principled 

• Sets high bars for rigor, correctness, and interpretability 

• Comfortable challenging narratives that are not supported by data 

• Communicates complex modeling concepts to executive audiences with clarity 

• Creates alignment between quantitative truth and business action 

Company Overview 

At Fidelity, we are focused on making our financial expertise broadly accessible and effective in helping people live the lives they want. We are a privately held company that places a high degree of value on creating a work environment that attracts top talent and reflects our commitment to integrity, inclusion, and excellence.