Data Scientist (Growth)

What You'll Actually Do

• Build and improve customer LTV and LTGP models. Predict lifetime value and lifetime gross profit from retention, expansion, usage, cost to serve and customer characteristics. Account for incomplete histories, young cohorts and sparse segments; revisit assumptions as customer behavior changes.

• Ship LTGP as the live bidding signal. Take the shadow model to a live value signal in Meta and Google, watch how the platforms respond, and keep improving it. By day 90 this is live, not a write-up.

• Validate predictions before they drive decisions. Backtest on historical cohorts without leaking future information, check calibration and quantify uncertainty. Monitor model performance and define when evidence is too weak to act on.

• Guide acquisition and pricing. Turn predicted LTV, cost to serve and payback into recommendations on acquisition spend, customer segments and pricing. Make assumptions and trade-offs explicit.

• Model attribution and incrementality. Connect acquisition spend to customer lifetime value, quantify which channels bring valuable customers, and measure the incremental impact of marketing and sales activity. Use experiments and causal methods to guide budget allocation.

• Connect customer economics to financial planning. Use the customer models to inform growth, profit, runway and fundraising scenarios, with clear sensitivities rather than false precision.

• Make the models usable. Build tested, reproducible Python code and customer-lifecycle dashboards that explain the predictions, assumptions and uncertainty, so the team understands the whole customer lifecycle.

• Work closely with growth, sales, finance and the Data Engineer. Own the modeling and interpretation; partner on reliable datasets and shared metric definitions rather than owning warehouse infrastructure.

Why This Role Is Different

• You report to Fryderyk, our CEO, initially. Your mandate spans growth, sales and finance, rather than sitting inside one channel team.

• Customer LTV is the primary focus. Your models directly inform where we acquire customers, how we price and where we invest in sales.

• Spend decisions sit with Matt, who owns paid acquisition. You sync with him weekly, and your signal is what Meta and Google see.

• You work alongside the people making those decisions in Warsaw and see how the predictions perform in practice.

• Your models and metric definitions become part of a data layer used by people and AI.

Who You Are

• Strong statistical and quantitative modeling skills. You can reason about retention, expansion and lifetime value, choose appropriate methods, and explain their assumptions and limits.

• You know how to validate a model: backtesting, calibration, uncertainty estimation, leakage prevention and performance under changing customer behavior.

• Depth in experimentation and causal inference. You can distinguish correlation, attribution and incremental impact, and explain what the available data cannot establish.

• Advanced SQL and strong Python engineering skills. You can build, test and maintain reproducible modeling code, not just explore data in a notebook.

• Commercial judgment. You can turn a prediction into a defensible acquisition, pricing or sales-investment decision, including a recommendation not to act when evidence is weak.

• Agentic engineering is part of your daily workflow. You use coding agents to move faster while taking responsibility for the methods, code and conclusions.

• You work directly with decision-makers, challenge assumptions and follow through on whether your recommendations worked.

• Based in Warsaw or willing to relocate, working onsite with the team.

Even Better If

• A background in quantitative research, statistics, econometrics or another field where predictions are rigorously tested against outcomes.

• Experience with customer lifetime value, survival or retention modeling, and SaaS or usage-based economics.

• You've run incrementality tests or built marketing-mix models on real ad spend.

• You've built something with LLMs that went beyond a demo.

Compensation

Top-of-the-market salary and the kind of ownership that only exists at this stage.

This is an onsite role in Warsaw, working alongside the growth team and finance.