{"id":1018428,"url":"https://alion.io/job/viktor-growth-data-scientist","title":"Data Scientist (Growth)","company":{"id":7156,"name":"Viktor","domain":"viktor.com","url":"https://alion.io/company/getviktor-com","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":91,"open_postings":28,"ghost_share":0.143,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":54,"computed_at":"2026-11-04T06:34:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":57000,"max_usd":102000,"period":"year","method":"role_country_seniority_unknown","sample_n":367},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"ClickHouse","optional":false},{"name":"Microsoft Teams","optional":false},{"name":"Python","optional":false},{"name":"Slack","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-09-18T00:51:37Z","employer_posted_date":"2026-09-18","last_verified_at":"2026-11-04T22:43:22Z","board_verified":true,"closed_at":null,"days_open":48,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":47},"description":"About Viktor\nViktor 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.\nThe team is small. The scope is not.\nThe Short Version\nYou 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.\nThe 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.\nYou'll work onsite in Warsaw alongside Fryderyk, Matt, the growth team and finance, reporting to Fryderyk initially.\nThe Hard Part\nOur 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.\nYou 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.\nAttribution 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.\nWhat You'll Actually Do\nBuild 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.\n\nShip 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.\n\nValidate 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.\n\nGuide 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.\n\nModel 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.\n\nConnect customer economics to financial planning. Use the customer models to inform growth, profit, runway and fundraising scenarios, with clear sensitivities rather than false precision.\n\nMake 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.\n\nWork 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.\n\nWho You Are\nStrong statistical and quantitative modeling skills. You can reason about retention, expansion and lifetime value, choose appropriate methods, and explain their assumptions and limits.\n\nYou know how to validate a model: backtesting, calibration, uncertainty estimation, leakage prevention and performance under changing customer behavior.\n\nDepth in experimentation and causal inference. You can distinguish correlation, attribution and incremental impact, and explain what the available data cannot establish.\n\nAdvanced SQL and strong Python engineering skills. You can build, test and maintain reproducible modeling code, not just explore data in a notebook.\n\nCommercial 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.\n\nAgentic engineering is part of your daily workflow. You use coding agents to move faster while taking responsibility for the methods, code and conclusions.\n\nYou work directly with decision-makers, challenge assumptions and follow through on whether your recommendations worked.\n\nBased in Warsaw or willing to relocate, working onsite with the team.\n\nWhy This Role Is Different\nYou report to Fryderyk, our CEO, initially. Your mandate spans growth, sales and finance, rather than sitting inside one channel team.\n\nCustomer LTV is the primary focus. Your models directly inform where we acquire customers, how we price and where we invest in sales.\n\nSpend decisions sit with Matt, who owns paid acquisition. You sync with him weekly, and your signal is what Meta and Google see.\n\nYou work alongside the people making those decisions in Warsaw and see how the predictions perform in practice.\n\nYour models and metric definitions become part of a data layer used by people and AI.\n\nEven Better If\nA background in quantitative research, statistics, econometrics or another field where predictions are rigorously tested against outcomes.\n\nExperience with customer lifetime value, survival or retention modeling, and SaaS or usage-based economics.\n\nYou've run incrementality tests or built marketing-mix models on real ad spend.\n\nYou've built something with LLMs that went beyond a demo.\n\nTech\nClickHouse, Hex, Python, PostHog, Stripe data.\nHow We Work\nSmall 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.\nWhy Viktor\nWe're building an AI employee that does real work across a company's tools.\nThis is a rare window: everyone here owns something real. Not a task. A surface of the company that customers depend on.\nThat doesn't last forever. Right now, it's still true.\nCompensation\nTop-of-the-market salary and the kind of ownership that only exists at this stage.\nThis is an onsite role in Warsaw, working alongside the growth team and finance.","description_format":"text","description_chars":6493,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["AI Agents"],"lifecycle":[{"event":"open","at":"2026-09-18T01:27:57Z"}],"visa":[],"liveness":{"score":50,"band":"ok","label":"Likely open","p_open":1,"p_active":0.667,"p_room":0.75,"age_days":47,"expected_fill_days":54,"reasons":["conf:2","win:late"],"computed_at":"2026-11-04T06:34:25Z"},"pay":null,"html_url":"https://alion.io/job/viktor-growth-data-scientist","json_url":"https://alion.io/job/viktor-growth-data-scientist.json","meta":{"generated_at":"2026-11-05T02:14:05Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":2842,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-06T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":7156},"rest":"https://alion.io/mcp/rest/get_company?id=7156"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fviktor-growth-data-scientist"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fviktor-growth-data-scientist"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fviktor-growth-data-scientist"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/viktor-growth-data-scientist\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fviktor-growth-data-scientist"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":1018428}},{"tool":"search_jobs","arguments":{"company":"viktor.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":1018428},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}