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NYC Office, New York, United States
Posted · 28.08.2026
Ashby (US)

# Senior Data Scientist

Confido

The brands on your kitchen shelf are run by people doing extraordinary work that nobody sees.
We're building the platform they run on, and a brand they'll be proud to be part of.
Confido is the AI operating system that runs cash application, deductions, trade and planning in one place for CPG brands. That means Finance, Sales, and Ops finally work on the same number.
We’re trusted by 250+ brands managing $30B+ in revenue, including Daisy, OLIPOP, Simple Mills, Idahoan, and more. We've achieved best-in-class growth and recently raised a $55M Series B led by Insight Partners to accelerate our momentum.
If you want your work on shelves everywhere — and outsized ownership while you build — we'd love to meet you.
THE ROLE
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.
WHAT YOU'LL WORK ON
• 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
ABOUT YOU
• 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 (we use Snowflake and dbt)
• You care whether your analysis is right, not just whether it runs — you validate, sanity-check, and communicate uncertainty
• You can explain a model to a non-technical brand operator and defend it to an ML engineer
BONUS
• Experience with retail, CPG, supply chain, or demand-planning data
• Experience shipping models into production systems (vs. offline analysis only)
• Familiarity with forecasting at scale (hierarchical forecasting, intermittent demand)
Stack: Python
• SQL
• Snowflake
• dbt
• AWS
🌴 PERKS + BENEFITS
• Equity — own a meaningful piece of the company you’re helping build
• Fully paid health coverage through Aetna — we cover 100% of employee premiums
• Top-tier dental and vision coverage through Guardian
• Carrot Fertility Pro — comprehensive fertility and family-forming support
• 12 weeks of paid parental leave
• Unlimited PTO — plus regular 4-day holiday weekends we actually take
• 401(k) through Vestwell
• Paid relocation support — we’ll help you make the move to NYC
• Fully equipped workspace from day one — laptop, monitor, keyboard, and a $200 stipend to personalize your setup
• Team perks — catered Friday lunches, team dinners, and unlimited coffee + snacks featuring products from the brands we work with
Confido provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

This job was verified from Ashby (US). Applications are completed on the original source.

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