viktor · Warsaw ·
Build and validate customer lifetime-value models that guide acquisition spend, pricing, and sales investment at Viktor, an early-stage AI-teammate startup. Day to day: statistical and causal modeling in Python/SQL (ClickHouse, PostHog, Stripe data), backtesting and calibration, attribution/incrementality analysis, and lifecycle dashboards — onsite in Warsaw.
bestow · Remote
Finom · Spain
Purpose Unlimited
Vortexa · London, England, United Kingdom
Viktor is the AI employee. 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.
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.
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.
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.
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.
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.
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.
ClickHouse, Hex, Python, PostHog, Stripe data.
Small team, high trust, low process. Decisions are made by owners, not committees. You will ship your first week. You will talk to users your first day.
We're building an AI employee that does real work across a company's tools.
This is a rare window: everyone here owns something real. Not a task. A surface of the company that customers depend on.
That doesn't last forever. Right now, it's still true.
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.
Attain · Chicago, IL; New York, NY; Redwood City, CA