- Pay
- $231–385k
- Where
- New York City, NY · Collegeville, PA
- Level
- Senior
ROLE PURPOSE
Lead the Data Science Products team: the team that turns proven data science capabilities into owned, governed, reusable products and evolves them over time (v1.0 → N). The portfolio is the measurement, optimization, and forecasting engine behind commercial investment decisions across USA and International: marketing mix and incrementality measurement, promotional and media investment optimization, and forecasting. Each product line runs on sophisticated production pipelines and is on a deliberate path from analyst-run engines to agentic solutions. The Lead builds and runs the portfolio through hands-on technical Product Owners, establishes the operating model that lets Data Science and analytics teams pull the products through for themselves with consistency and rigor, and is accountable for the technical quality of the models and the agents alike.
KEY RESPONSIBILITIES
Portfolio leadership — Own the strategy, roadmap, and health of the full product portfolio across measurement, optimization, forecasting and more; allocate scarce capacity across product lines; inform what to build once and reuse versus what stays bespoke.
Operating model & CoE authority — Stand up and run the global/shared model: methodology standards, the shared codebase, intake and prioritization, quality standards, and best-practice forums, so the team operates as a true CoE that enables scale and consistency across markets.
Measurement standards — Own the standard for how commercial impact is measured, versioned, and validated across the portfolio, including how incrementality tests and experiments are used to calibrate and validate the models, so that numbers entering investment decisions and leadership reviews are trusted and developed with rigor.
Technical leadership — Set and review modeling methodology across the portfolio: Bayesian and causal marketing mix modeling, constrained optimization, incrementality and experiment design, and time-series and hierarchical forecasting. Apply engineering and architectural standards across product lines; ensure a common, well-governed GitHub codebase and sound MLOps practice: model registry, versioning, monitoring, retraining, and reproducible pipelines.
Agentic products — Lead the evolution of each product line from analyst-run engine to agentic solution: agents that answer investment and forecast questions in natural language, run scenarios, explain drivers, and propose allocations within guardrails, grounded in the production pipelines. Own the portfolio standards for how these are built: grounding in model outputs, evaluation, guardrails, cost, traceability, and human oversight.
Lifecycle & hand-offs — Interface with Applied Intelligence (0 → v1.0 incubation) so proven capabilities graduate cleanly into product ownership, and with Operations & Delivery and Business-Aligned so products are run and tailored at scale.
Senior stakeholder leadership — Be the senior point of contact for the USA and International commercial leadership the products serve, and for Finance and brand leadership where measurement and forecasts enter investment and planning decisions; ensure equitable, consistent service across both markets and represent the portfolio with executive stakeholders.
People & capability — Hire, develop, and retain a small, dense team of senior technical Product Owners; steward headcount carefully, funding new product lines without hollowing out established ones.
BASIC QUALIFICATIONS
Education & experience — Bachelor’s degree in a quantitative discipline with 12+ years of relevant experience; OR Master’s degree with 10+ years; OR PhD with 7+ years.
Machine learning — 8+ years hands-on building and deploying statistical and machine learning models in production, with demonstrated depth in causal inference and marketing mix or incrementality modeling (Bayesian methods, adstock and saturation, hierarchical pooling, response-curve estimation and constrained optimization) and working fluency in…