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Viktor is building an AI teammate that lives in Slack and Microsoft Teams, connecting to thousands of tools and doing real work across finance, marketing, ops, and engineering. The company is small with ambitious scope, and you'll join a team that owns meaningful surfaces of the product.
In this role, you'll own customer lifetime value modeling and use those predictions to guide acquisition strategy, pricing decisions, and sales investment allocation. You'll predict what customers will be worth over their lifetime by connecting retention, expansion, and product usage patterns to LTV, then validate whether those predictions hold up in practice.
Key responsibilities include:
- Build and improve customer LTV models that account for incomplete histories, young cohorts, and sparse data segments. Revisit assumptions as customer behavior and product surfaces change.
- Validate predictions rigorously before they drive decisions: backtest on historical cohorts, check calibration, quantify uncertainty, and monitor performance over time.
- Turn predicted LTV, cost to serve, and payback into actionable recommendations on acquisition spend, customer segments, and pricing strategy.
- Model attribution and incrementality to separate which channels bring valuable customers from which drive incremental impact. Use experiments and causal methods to guide budget allocation.
- Connect customer economics to financial planning, informing growth, profit, runway, and fundraising scenarios with clear sensitivities.
- Build tested, reproducible Python code and customer-lifecycle dashboards that explain predictions, assumptions, and uncertainty to the broader team.
- Work closely with growth, sales, finance, and the data engineer. Own modeling and interpretation; partner on reliable datasets and shared metric definitions.
You'll report initially to Fryderyk (CEO), with a mandate spanning growth, sales, and finance rather than a single channel. You'll work onsite in Warsaw alongside the decision-makers and see how your predictions perform in practice. The role emphasizes shipping quickly, talking to users early, and taking responsibility for methods and conclusions.
Requirements:
- Strong statistical and quantitative modeling skills; ability to reason about retention, expansion, and lifetime value with appropriate methods.
- Proven ability to validate models: backtesting, calibration, uncertainty estimation, leakage prevention, and performance under changing customer behavior.
- Depth in experimentation and causal inference; ability to distinguish correlation, attribution, and incremental impact.
- Advanced SQL and strong Python engineering skills; ability to build, test, and maintain reproducible modeling code.
- Commercial judgment to turn predictions into defensible decisions, including recommending inaction when evidence is weak.
- Agentic engineering as part of daily workflow; comfort using coding agents while taking responsibility for methods and conclusions.
- Ability to work directly with decision-makers, challenge assumptions, and follow through on whether recommendations worked.
- Based in Warsaw or willing to relocate; working onsite with the team.
Even better if you have:
- Background in quantitative research, statistics, econometrics, or similar field where predictions are rigorously tested.
- Experience with customer lifetime value, survival, or retention modeling in SaaS or usage-based economics.
- Attribution, marketing-mix modeling, or incrementality measurement on real marketing or sales investment.
- Experience building with LLMs beyond demos.
Tech stack: ClickHouse, Hex, Python, PostHog, Stripe data.
