Data Analyst

We are looking for a Global Data Analytics Expert to own the forecasting, financial modelling and data intelligence behind our product. This person will work closely with the Co-founder and Product and Engineering teams to build growth forecasts, brand-level P&Ls, profitability models and valuation frameworks for e-commerce brands on our platform.

This is not a traditional reporting or BI role. The focus is on understanding how e-commerce businesses grow, modelling their financial and marketing performance, and translating that expertise into repeatable product logic and AI-powered intelligence.

The person will be accountable for the quality, accuracy and commercial reliability of the numbers our product generates, ensuring that every forecast is backed by documented assumptions, validated data and clearly defined confidence levels.

Key Responsibilities

1. Growth Forecasting and Financial Modelling

• Build and own 12-month growth forecasts and brand-level P&Ls covering revenue, COGS, contribution margins, operating costs and profitability.
• Develop unit economics, breakeven, working capital, pre-funding prediction and valuation models.
• Maintain forecasts as actual performance data comes in, analyse variances and continuously improve model accuracy.
• Translate financial and growth data into actionable recommendations for founders and business leaders.2. Brand Intelligence and Benchmarking
• Build and maintain a benchmark library across e-commerce categories, markets, growth stages and marketing channels.
• Evaluate CAC, AOV, LTV, payback period, contribution margin, retention and channel-level performance.
• Analyse paid, organic and offline channels, distinguishing blended returns from incremental impact.
• Develop reliable forecasting approaches for brands with incomplete, self-reported or limited historical data.3. Data Quality and Model Validation
• Define data schemas, metric definitions, assumptions and validation rules for structured commercial inputs.
• Establish checks that identify inaccurate, inconsistent or implausible numbers before they reach customers.
• Measure forecast accuracy against actual outcomes, investigate errors and recalibrate models.
• Define confidence levels, limitations and human sign-off requirements for commercially sensitive outputs.4. Productisation and AI Integration
• Translate forecasting and financial models into product logic in collaboration with Product and Engineering.
• Define how models, benchmarks and assumptions are encoded into the platform so outputs are repeatable, traceable and explainable.
• Specify AI agent workflows, prompt templates, knowledge sources, evaluation criteria and human-in-the-loop review processes.
• Establish clear boundaries for what AI agents can calculate, recommend or assert, and what requires human verification.
• Design how forecast and performance insights are presented to different user groups across the product ecosystem.5. Stakeholder Collaboration and Enablement
• Work closely with the Co-founder, Product, Engineering and delivery teams to ensure the models reflect real commercial requirements.
• Present and defend modelling assumptions, financial outputs and uncertainty to founders and business stakeholders.
• Document modelling standards, metric definitions and delivery requirements.
• Build quantitative literacy across the business so teams can interpret and use forecasts correctly.

Ideal Candidate Profile

• 6–8 years of experience in growth, commercial, marketing or product analytics, ideally within DTC/e-commerce, marketplaces or products serving these businesses.
• Experience working across multiple brands, with a strong understanding of differences in category economics, growth stages and marketing performance.
• Proven ownership of forecasting, unit economics, financial modelling or media mix models used to make real commercial decisions.
• Strong understanding of brand-level P&Ls, revenue forecasting, CAC, AOV, COGS, contribution margin, LTV, payback, breakeven and working capital.
• Strong statistical grounding, including the ability to quantify uncertainty, validate assumptions and work with incomplete datasets.
• Hands-on proficiency in SQL and Python, with experience building and validating analytical models.
• Experience collaborating with Product and Engineering to operationalise models, calculations or metric definitions in production.
• Ability to translate complex quantitative analysis into clear commercial recommendations and defend assumptions with senior stakeholders.
• Strong documentation skills and the ability to establish standards that other teams can consistently follow.