Senior Data Scientist, Strategic Modeling & Simulation

At Apple, many of the most consequential decisions are made long before products launch, features ship, or investments are approved. We are seeking a Senior Data Scientist, StrategicModeling & Simulation to help quantify the future impact of strategic decisions under uncertainty.

This role combines strategic simulation, predictive modeling, machine learning, impact estimation, forecasting, and quantitative decision science to help leadership evaluate opportunities, understand trade-offs, and make better long-term investment decisions. You will develop the models that connect experimentation learnings, behavioral signals, and business outcomes into forward-looking simulations that support strategic planning and resource allocation.

As Apple expands investments in AI-powered experiences and intelligent systems, this role will help assess the long-term implications of emerging technologies and evolving customer behaviors while supporting strategic decision-making under uncertainty.

The ideal candidate combines strong quantitative rigor with systems thinking, scientific curiosity, and a passion for solving complex product and business problems through modeling and simulation.

Description

As a Senior Data Scientist, Strategic Modeling & Simulation, you will develop simulation systems and strategic modeling frameworks that estimate the long-term impact of product, growth, and business decisions.

You will work across experimentation, product, marketing, consumer research, engineering, finance, and leadership teams to develop predictive models, impact estimation frameworks, and strategic scenario simulations that support decision-making under uncertainty.

This role sits at the intersection of machine learning, economics, forecasting, simulation, operations research, and quantitative strategy. You will help develop forecasting and simulation capabilities that support both traditional product investments and emerging technology initiatives where long-term outcomes are uncertain and difficult to measure directly.

The ideal candidate possesses strong technical depth, excellent scientific reasoning skills, and the ability to communicate quantitative insights to executive audiences.

Responsibilities:

Strategic Simulation: Develop simulation frameworks that estimate future product, growth, subscriber, engagement, retention, and revenue outcomes under alternative strategic scenarios.

Product Investment Modeling: Build product investment simulations that estimate the long-term impact of proposed features, roadmap initiatives, and engineering investments before resources are committed.

Impact Modeling & Opportunity Sizing: Estimate the impact of product, growth, and operational investments, including conversion elasticity, feature ROI, subscriber growth, retention lift, and revenue impact.

Predictive Machine Learning: Develop predictive models for retention, churn, engagement, subscriber growth, conversion, lifetime value, and behavioral outcomes that serve as in puts into simulations and strategic planning.

Short-Term to Long-Term Metric Linkage: Build models that connect short-termexperimentation outcomes and behavioral signals to long-term retention, monetization, subscriber growth, and customer lifetime value.

Probabilistic Forecasting & Uncertainty Quantification: Develop forecasting and probabilistic modeling approaches that represent uncertainty, confidence ranges, scenario distributions, and sensitivity to assumptions.

Optimization & Resource Allocation: Develop quantitative approaches for portfolio planning, resource allocation, initiative prioritization, and constrained investment trade-off analysis.

Strategic Scenario Analysis: Evaluate trade-offs across competing strategic initiatives and communicate expected outcomes, risk ranges, assumptions, and decision implications.

Emerging Technology Impact Modeling: Develop frameworks that estimate the potential long-term impact of new technologies, AI-powered experiences, recommendation systems, and adaptive products on engagement, retention, subscriber growth, and business outcomes.

Cross-Functional Collaboration: Partner with Product, Marketing, Experimentation Science, Consumer Research, Engineering, Finance, and leadership teams to support strategic planning and investment decisions.

Preferred Qualifications

PhD in Statistics, Computer Science, Economics, Operations Research, Data Science, Applied Mathematics, Industrial Engineering, or a related quantitative discipline.

Experience with simulation systems, probabilistic modeling, Bayesian methods, survival analysis, causal impact modeling, reinforcement learning concepts, or uncertainty quantification

Experience estimating long-term business impact, investment ROI, subscriber growth, retention compounding, or customer lifetime value.

Experience in Product Science, Applied Economics, Operations Research, Quantitative Research, Strategic Modeling, Decision Science, or related quantitative strategy functions

Experience building strategic modeling systems that combine experimentation evidence, predictive ML, behavioral signals, and business outcomes.

Experience modeling the impact of machine learning systems, recommendation systems, adaptive products, AI-powered experiences, or other complex adaptive systems

Publications or research contributions in venues such as KDD, CIKM, ICML, NeurIPS, WWW, WSDM, RecSys, AISTATS, or related conferences and journals.

Experience supporting executive-level strategic planning, portfolio prioritization, or investment decision-making.

Minimum Qualifications

Master's degree or higher in Statistics, Data Science, Computer Science, Operations Research, Economics, Applied Mathematics, Industrial Engineering, or a related quantitative discipline.

5+ years of experience in predictive modeling, simulation, forecasting, quantitative strategy, product science, applied economics, operations research, or related fields.