Senior Data Scientist

About the role

Confido is the AI infrastructure powering modern CPG — the platform that 200+ brands like OLIPOP, Simple Mills, Dr. Squatch, and Tropicana use to run everything from deductions to production planning. Finance, accounting, sales, and operations, unified in one system for the first time. We're growing 5x year over year with a small team in New York City; the people who join now will shape the product, the culture, and the company itself. If you want your work on shelves everywhere — and outsized ownership while you build — we'd love to meet you.

CPG brands live and die by their ability to predict what’s coming — how much product will sell, where demand will land, and what a promotion will actually do to the business. At Confido, you’ll build models that help brands forecast sales, price products, and shape promotional strategy. You’ll work with an unusually broad dataset spanning sell-through, inventory, deductions, trade promotions, and supply-chain signals from 200+ brands — much of it messy, real-world data that makes the problems genuinely interesting. The models you build will directly influence decisions behind the products you see on store shelves every day. As a Data Scientist, you'll turn that data into models and analyses that ship inside the product — not slide decks. You'll work alongside our AI/ML engineering team to move from exploratory analysis to production-grade insight.

Responsibilities

• Demand and sales forecasting — improving prediction quality across thousands of noisy, real-world retail time series
• Promotion effectiveness — measuring lift, price elasticity, and cannibalization so brands know which promotions actually pay
• Data quality and evaluation — building the statistical checks that keep our models honest as messy data flows in

Requirements

• 5+ years of hands-on data science experience with real-world (not benchmark) data
• Strong statistical foundations: regression, time-series methods, experiment design, uncertainty quantification
• Fluent in Python and SQL; comfortable working in a modern data stack