Senior Data Scientist, Impact Modeling & Analytics - US Program

Gates Foundation•Seattle, WA

About The Position

The Senior Data Scientist is responsible for building and maintaining impact accounting and analytical modeling capabilities that support data-driven investment decisions across the Data & AI Enablement Hub strategy. The role develops predictive models, effect-size extraction frameworks, cost-effectiveness estimates, and scenario analyses at the strategy, initiative, and body-of-work levels to support measurement, learning, evaluation, and resource allocation. The position applies advanced quantitative modeling, causal inference, and research synthesis, while ensuring that analytical approaches are reproducible, well documented, and designed to surface equitable outcomes for learners. The Senior Data Scientist partners with program officers, program strategy teams, measurement and evaluation colleagues, researchers, and division leadership to integrate insights into portfolio planning, grant design, strategy learning, and governance. The role also identifies gaps in analytical infrastructure and develops new capabilities, tools, or data partnerships to strengthen assessment of AI-enabled interventions at scale.

Requirements

Advanced degree or equivalent demonstrated experience, preferably in economics, statistics, public policy, education research, or a related quantitative field.
Mastery-level knowledge of quantitative modeling methods relevant to education and social impact, including causal inference, meta-analysis, effect-size estimation, cost-effectiveness analysis, and predictive modeling.
Significant experience building and maintaining analytical models that inform investment or program strategy decisions in education, social policy, or adjacent fields.
Demonstrated ability to move from research synthesis to decision-relevant outputs.
Significant experience advising program or leadership teams as a technical resource on impact analysis, cost-effectiveness modeling, and decision analytics.
Ability to translate complex quantitative findings for non-technical audiences.
Experience developing and applying reproducibility and documentation standards, including practices that enable peer review, assumption auditing, and equity assessment of model outputs.
Significant experience building collaborative partnerships with program teams, researchers, or institutional partners to co-develop analytical frameworks and integrate modeling insights into planning and governance.
Unrestricted work authorization in the country where the position is located; the Foundation does not provide immigration-related sponsorship or other immigration support.

Responsibilities

Build and maintain impact accounting models, including effect-size extraction frameworks, cost-effectiveness estimates, and scenario analysis tools, to support investment decisions across the portfolio.
Apply rigorous standards to identify measurable outcomes and track results over time.
Lead predictive modeling and forward-looking analysis at the strategy, initiative, and body-of-work levels to inform impact trajectories and resource allocation.
Contribute to the Insights Engine for AI Effectiveness by synthesizing research, market intelligence, and pilot data to identify patterns, risks, and opportunities.
Ensure analytical reproducibility, documentation, and equity; develop shared standards that support scrutiny of assumptions, auditing of methods, and assessment of outcomes for historically underserved learners.
Partner with Hub program officers and program strategy teams to apply modeling insights in portfolio planning and grant design, translating findings into actionable guidance.
Communicate complex model outputs and their implications to non-technical program audiences and leadership.
Collaborate with measurement, learning, and evaluation colleagues and division leadership to integrate impact accounting into strategy learning and governance processes.
Adapt analytical methods as data availability, strategic priorities, and field evidence evolve.
Identify gaps in analytical infrastructure and initiate development of modeling capabilities, tools, or data partnerships.
Support an inclusive culture through behaviors and actions, and escalate issues to appropriate stakeholders in a timely manner.