About the role
About the Role
We are looking for a rigorous, systems-minded quantitative leader to own our core commercial intelligence engine end-to-end. This is not a traditional dashboard-building or BI reporting seat. You will define, build, and encode the mathematical logic, unit-economic models, and predictive forecasting systems that power our product and drive real commercial decisions for our brands.
You will sit at the intersection of finance, data science, and product engineering, turning complex commercial data, messy founder inputs, and predictive models into defensible, automated product logic and AI agent workflows.
Key Accountabilities
1. Proprietary Systems: Growth Forecasts & P&Ls
Growth Forecasting: Build and own the methodology that takes a brand’s current performance and projects a defensible 12-month forecast, complete with a rigorous assumption set.
Financial Architecture: Construct brand-level P&Ls directly from the forecast, mapping revenue, COGS, contribution margin, and operating costs.
Variance & Calibration: Continuously run models against real outcomes, measure errors, track actuals against projections, and explain every variance.
2. Productization & Agentic Workflow Design
Product Logic: Translate sophisticated financial and statistical models into production-ready product logic so the system calculates and delivers outputs natively.
AI Agent Specification: Define what data agents compute, what internal data/benchmarks they draw on, and what assertions they are strictly barred from making.
Human-in-the-Loop Orchestration: Replace manual validation with automated product rules while establishing clear review gates for human sign-off before deliverables ship.
Data Schemas & Engineering Collaboration: Specify data structures, validation rules, and pipelines across structured commercial inputs, partnering directly with engineering to bring models to life in production.
3. Decision Framing & Commercial Rigor
Decision-First Modeling: Design every analysis backwards from the specific commercial decision it is meant to change.
Uncertainty & Stated Confidence: Size uncertainty statistically, state clearly what sparse or self-reported data cannot support, and attach a stated confidence level to every output.
Benchmark Library Management: Define and version the benchmark repository (category, stage, market) to evaluate brands accurately even when data is sparse.
4. Enablement & Commercial Communication
Cross-Functional Communication: Present complex numbers, models, and uncertainties to founders and operators with clarity and authority.
Quantitative Literacy: Raise the analytical bar across the business by teaching teams how to read and interpret model outputs correctly.
Documentation & Standards: Write airtight documentation and standards for data/finance deliverables so cross-functional delivery teams can execute autonomously.
What This Seat Is Not
Not a BI/Reporting Seat: Dashboards are merely one downstream output of this work; your primary focus is proprietary modeling and product architecture.
Not Research Without Action: We judge models by the decisions they actually changed, not academic complexity.
Not a Data Engineering Seat: You will specify pipelines, schemas, and logic for engineering to build—you won't be maintaining core infrastructure tables yourself.
Ideal Background & Qualifications
Must-Haves
Experience: 6–8 years in growth, marketing, commercial, or product analytics within DTC, e-commerce, marketplaces, or specialized platforms.
Proven Track Record: End-to-end ownership of a forecasting, unit-economics, or media-mix model that commercial teams relied on for high-stakes budget or strategic decisions. You can name the decisions your model changed and what followed.
DTC Unit Economics Mastery: Deep fluency in CAC, AOV, COGS, contribution margin, LTV, payback period, breakeven, retention cohorts, and working capital reconciliation.
Modelling with Incomplete Data: Demonstrated ability to build robust outputs from banded, self-reported, or partial founder inputs while explicitly quantifying confidence levels.
Statistical Rigor: Strong foundation in sizing uncertainty, managing sample limitations, and maintaining analytical integrity.
Technical Depth: Hands-on daily fluency in SQL and Python.
Product & Engineering Collaboration: Experience working directly with engineering teams to turn quantitative models or metric definitions into production software.
Communication: Exceptional presence and ability to defend underlying assumptions and model uncertainty under direct challenge.
Nice-to-Haves
Media mix modeling (MMM), incrementality testing, or causal measurement methodologies.
Applied machine learning experience (forecasting, propensity, or segmentation models in production).
Experience evaluating generative AI or multi-agent system outputs.
Familiarity with modern data stack layers (Snowflake, BigQuery, dbt) and attribution platforms (Triple Whale, Northbeam, Rockerbox).
Exposure to multi-brand ecosystems via agency, consulting, or platform environments.
Experience in early-stage product environments where models and products were co-developed from scratch.
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