Growth Data Scientist

About Viktor Viktor is the AI teammate. He lives in Slack and Microsoft Teams, connects to thousands of tools, and does real work for real companies: finance, marketing, ops, engineering. We're building the product that replaces half the SaaS stack. The team is small. The scope is not.   The Short Version You predict what customers will be worth over their lifetime, and use those predictions to guide acquisition, pricing and sales investment. This is a quantitative modeling role: you connect retention, expansion and product usage to customer lifetime value, test whether the predictions hold up, and make clear when they are too uncertain to act on. You'll work onsite in Warsaw alongside Fryderyk, Matt, the growth team and finance, reporting to Fryderyk initially.   The Hard Part Our customers haven't finished their lifetimes. Many cohorts are young, histories are incomplete, and some segments have little data. Meanwhile, new product surfaces change how customers use Viktor, expand and retain. A model that fits yesterday's customers may not predict tomorrow's. You need to distinguish durable signals from noise, estimate value before a cohort matures, and show how much confidence a decision deserves. Backtesting, calibration and uncertainty are part of the work, not checks added at the end. Attribution is another hard problem: a channel can appear to bring valuable customers without causing additional value. You'll separate attributed revenue from incremental impact, including the effect of sales activity, so we know where another dollar of investment is likely to pay back.   What You'll Actually Do Build and improve customer LTV models. Predict lifetime value from retention, expansion, usage and customer characteristics. Account for incomplete histories, young cohorts and sparse segments; revisit assumptions as customer behavior changes. 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. By day 90, the team should have dashboards that help it understand 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.

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