Global Data Analytics Expert

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.

The best time to join Valerie was yesterday. The next best time is now 🤓

👋Our Hiring Process

We want interviews to be valuable for both sides. Throughout the process, you'll meet the people you'll work with, learn more about the role, and get a chance to understand how we think and operate.

Our process typically includes:

Introductory conversation with our Talent team <> 30 mins

Hiring Manager interview <> 30-45 mins

Role-specific assessment or practical exercise (where applicable)

Cross-functional or stakeholder interviews <> 30-45 mins

Final conversation with leadership <> 30-45 mins

We'll always let you know what to expect before each stage.

💪🏾What You Can Expect

Meaningful work with visible impact

High ownership from day one

Collaboration with experienced founders, operators, and specialists

A team that values curiosity, initiative, and continuous improvement

Competitive compensation and the tools you need to do your best work

Opportunities to grow as Valerie grows

Valerie is committed to building an inclusive workplace where people from different backgrounds, experiences, and perspectives can thrive. We hire based on skills, potential, and the ability to contribute to our team.