Head of Predictions & Optimization

THE ROLE
Tell brands what to charge, which promotions to run, and where. Back it with data almost no one else has.

Confido sees what's actually happening in the physical world of CPG: shipments, depletions, inventory, consumption, what's on the shelf, merchandising, deductions, the demographics of who lives near each store, and forward-looking promo plans. That means rich actuals and a real view of the future, across hundreds of brands. Most of this data isn't publicly available anywhere.

As Head of Prediction & Optimization, you'll build and lead Confido's data science function, starting with our newest product area: Revenue Growth Management (RGM). You'll own the models that turn a detailed picture of the business into decisions brands actually act on, and you'll grow the team to 3+ data scientists.

Location: New York, NY (Relocation supported)

WHAT YOU'LL DO
Lead pricing and trade optimization for RGM, including price elasticity, promo lift, cannibalization, and halo effects, so you can answer "how much should I charge for this?"

Build promotion recommendations that get specific. Should a promo run 4 weeks or 8? Should it be a deeper discount, a BOGO, or buy-3-get-1? Which regions, retailers, and accounts should get it?

Turn predictions into prescriptions by building optimization that works under real constraints like trade budgets, margin targets, and retailer calendars

Measure what actually worked, using post-event analysis, causal inference, and baselines that brands trust enough to change their plans

Partner with our AI/ML engineers to take models to production, and set the bar for backtesting, evaluation, and monitoring

Build the team: hire and mentor data scientists, and set the roadmap and technical direction alongside our CTO and AI research lead

WHAT WE'RE LOOKING FOR
Required

7+ years of applied data science or predictive modeling, including experience leading projects or people

Deep expertise in statistical modeling and ML for pricing, demand, or forecasting, such as elasticity, causal inference, or uplift modeling

Experience building optimization or decision-support models, beyond predictions alone

Strong Python and SQL, plus rigor about validation: you know how to avoid leakage and when to distrust a good-looking result

The ability to turn a model into a clear recommendation a non-technical business user will act on

Nice to have

RGM, trade promotion, pricing, or revenue management experience in CPG, retail, or marketplaces

Experience with syndicated or retailer data (Nielsen, SPINS, Circana, retailer POS)

Taking models to production yourself. We're indexed on modeling, but end-to-end is a plus

Operations research or mathematical optimization (MIP, solvers)

Startup or 0-to-1 team-building experience