The Role
Simon & Schuster is investing in Data & Decision Intelligence to embed data, tools, and automation into everyday work and enable faster, better-informed decisions. The Senior Decision Scientist, Forecasting & Optimization will improve decisions across marketing effectiveness, demand forecasting, pricing, and supply-chain planning. This role combines causal methods, optimization, business judgment, and responsible AI to move from evidence to action quickly and deliver measurable outcomes.
What You Will Do
Frame the decision. Turn ambiguous questions into clear decision problems, objectives, constraints, hypotheses, success measures, and analytical plans tied to business outcomes.
Build robust forecasts. Develop time-series, machine-learning, hierarchical, probabilistic, and cold-start approaches; quantify uncertainty across relevant levels and horizons, and backtest performance.
Measure marketing impact. Design experiments and apply causal-inference and quasi-experimental methods to estimate incrementality, causal impact, and return from marketing, publicity, promotions, and other interventions.
Optimize pricing and investment. Use elasticity models, simulation, mathematical optimization, and scenario analysis to improve pricing, promotion, and resource-allocation decisions within business constraints.
Optimize supply-chain decisions. Translate demand distributions into practical inventory, production, reprint, allocation, buffer-stock, and warehouse recommendations that balance service, cost, and risk.
Use AI to accelerate delivery. Apply approved generative AI and AI-assisted tools to research, code, develop features, test, document, and prototype faster; establish evaluation and human-review controls.
Own the solution lifecycle. Build, package, deploy, monitor, troubleshoot, and maintain models, decision services, and automated workflows, including testing, versioning, drift monitoring, and refresh processes.
Drive adoption and value. Communicate recommendations, uncertainty, and tradeoffs clearly; embed solutions into workflows, measure realized outcomes, and mentor colleagues.
What You Bring
Relevant experience. 6+ years in decision science, data science, applied economics, operations research, forecasting, quantitative analytics, or a related field.
Forecasting depth. Advanced Python and SQL with strong experience in time-series modeling, probabilistic forecasting, hierarchical forecasting, temporal validation, and model evaluation.
Causal measurement. Experience estimating marketing incrementality and causal impact through experimentation, quasi-experimental methods, or causal inference.
Optimization depth. Experience with pricing, elasticity, mathematical optimization, simulation, inventory, supply chain, resource allocation, or related constrained decisions.
End-to-end production capability. Experience moving models from notebook to production using modular code, APIs or batch workflows, packaging or containers, automated tests, Git, CI/CD, model tracking, monitoring, and cloud deployment.
Practical AI experience. Demonstrated use of generative AI or AI-assisted development to accelerate analytical work or product delivery, with systematic evaluation and responsible-use controls.
Business leadership. Ability to connect model outputs to decisions, balance rigor with speed, communicate uncertainty and tradeoffs, and lead across technical and business teams.
Preferred
Modern solution patterns. Experience with orchestration, feature management, real-time services, automated retraining, model registries, or decision-support applications.
Industry or academic background. Publishing, media, retail, or consumer-products experience; advanced quantitative degree or equivalent experience.
An Inclusive Workplace. We welcome candidates with different paths and perspectives. If you do not meet every preferred qualification, we still encourage you to apply.