Data Scientist (Growth)

About the role

ABOUT VIKTOR

Viktor is the AI employee that lives in Slack and Microsoft Teams, connects to thousands of tools, and does the work: reports, dashboards, code, campaigns. Our mission is to give every company an AI employee.

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.

The first place those predictions land is bidding: we are moving to lifetime gross profit, LTGP, as the value Meta and Google optimize against. A first model runs in shadow today. You take it live and own it.

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 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.