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. \\n\\nThis 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. \\n\\nAs 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. \\n\\nThe 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.

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. \n\nYou 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. \n\nThis 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. \n\nThe ideal candidate possesses strong technical depth, excellent scientific reasoning skills, and the ability to communicate quantitative insights to executive audiences.

Master's degree or higher in Statistics, Data Science, Computer Science, Operations Research, Economics, Applied Mathematics, Industrial Engineering, or a related quantitative discipline.\n5+ years of experience in predictive modeling, simulation, forecasting, quantitative strategy, product science, applied economics, operations research, or related fields.\nStrong expertise in statistical modeling, machine learning, predictive analytics, forecasting, and quantitative reasoning.\nExperience building predictive models such as retention, churn, conversion, engagement, or lifetime value models.\nExperience with simulation, scenario analysis, impact estimation, strategic modeling, or long-term value estimation.\nStrong Python programming skills and experience with modern machine learning or statistical modeling ecosystems.\nAbility to work with large-scale behavioral, product, business, survey, or experimentation datasets.\nStrong communication skills and ability to translate complex quantitative modeling outputs into clear decision guidance for leadership audiences.

PhD in Statistics, Computer Science, Economics, Operations Research, Data Science, Applied Mathematics, Industrial Engineering, or a related quantitative discipline.\nExperience with simulation systems, probabilistic modeling, Bayesian methods, survival analysis, causal impact modeling, reinforcement learning concepts, or uncertainty quantification\nExperience estimating long-term business impact, investment ROI, subscriber growth, retention compounding, or customer lifetime value.\nExperience in Product Science, Applied Economics, Operations Research, Quantitative Research, Strategic Modeling, Decision Science, or related quantitative strategy functions\nExperience building strategic modeling systems that combine experimentation evidence, predictive ML, behavioral signals, and business outcomes.\nExperience modeling the impact of machine learning systems, recommendation systems, adaptive products, AI-powered experiences, or other complex adaptive systems\nPublications or research contributions in venues such as KDD, CIKM, ICML, NeurIPS, WWW, WSDM, RecSys, AISTATS, or related conferences and journals.\nExperience supporting executive-level strategic planning, portfolio prioritization, or investment decision-making.