Head of Predictions & Optimization

Confido•New York, NY

9d•$250,000 - $350,000•Onsite

About The Position

Confido is the AI infrastructure powering modern CPG, providing a unified system for finance, accounting, sales, and operations. The platform is used by over 200 brands for various functions, including deductions and production planning. The company is experiencing rapid growth and is looking for individuals to shape its product, culture, and future. This role offers significant ownership and the opportunity to impact products found on shelves everywhere. As the Head of Prediction & Optimization, you will lead Confido's data science function, focusing on the Revenue Growth Management (RGM) product area. You will be responsible for developing models that translate business insights into actionable decisions for brands and will grow the data science team to include at least three members. The role is based in New York City, with relocation support provided.

Requirements

7+ years of applied data science or predictive modeling experience, including leadership of projects or people.
Deep expertise in statistical modeling and ML for pricing, demand, or forecasting (e.g., elasticity, causal inference, uplift modeling).
Experience building optimization or decision-support models.
Strong proficiency in Python and SQL.
Rigor in validation, including avoiding leakage and identifying unreliable results.
Ability to translate model outputs into clear recommendations for non-technical business users.

Nice To Haves

Experience in RGM, trade promotion, pricing, or revenue management within CPG, retail, or marketplaces.
Experience with syndicated or retailer data (Nielsen, SPINS, Circana, retailer POS).
Experience taking models to production.
Knowledge of operations research or mathematical optimization (MIP, solvers).
Experience in a startup environment or building a team from scratch.

Responsibilities

Lead pricing and trade optimization for RGM, including price elasticity, promo lift, cannibalization, and halo effects.
Build specific promotion recommendations, considering duration, discount levels, type (e.g., BOGO), and targeted regions, retailers, and accounts.
Develop optimization models that function under real-world constraints such as trade budgets, margin targets, and retailer calendars.
Conduct post-event analysis, causal inference, and establish trusted baselines to measure the effectiveness of strategies.
Collaborate with AI/ML engineers to deploy models into production, establishing standards for backtesting, evaluation, and monitoring.
Build and mentor a team of data scientists, defining the roadmap and technical direction in conjunction with the CTO and AI research lead.