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Data Scientist (Growth)

Viktor· Product & Engineering
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WarsawFull-timePosted 4w ago

About the role

Data Scientist focused on predicting customer lifetime value (LTV) and lifetime gross profit (LTGP) to guide acquisition, pricing and sales investment. Owns live LTGP bidding signal, validates predictions, and collaborates with growth, sales, finance and Data Engineer to align models with business decisions.

Responsibilities

  • 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.
  • 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.

Qualifications

  • 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.

Skills

Required:Statistical and quantitative modelingModel validationExperimentation and causal inferenceSQLPythonCommercial judgmentCommunication with decision-makersTeam collaboration
Last updated Oct 20, 2026. Interested? Apply on the company site.
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