{"id":520789,"url":"https://alion.io/job/higgsfield-data-science-product-ml-engineer-personalization-monetization","title":"Data Scientist, Product","company":{"id":66037,"name":"Higgsfield","domain":"higgsfield.ai","url":"https://alion.io/company/higgsfield","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":86,"open_postings":14,"ghost_share":0,"stale_share":0.5,"repost_share":0.071,"time_to_fill_p50_days":55,"computed_at":"2026-10-27T05:46:45Z"}},"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":["Almaty, Kazakhstan"],"countries":["KZ"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Feature Store","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-09-07T19:17:23Z","employer_posted_date":"2026-09-07","last_verified_at":"2026-10-28T02:00:04Z","board_verified":true,"closed_at":null,"days_open":50,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":50},"description":"Why work at Higgsfield AI?\nHiggsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 25M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like.\nWhat This Role Means at Higgsfield\nRight now we treat 25M+ people as one user. The same paywall, the same discount, the same lifecycle email, the same retention effort. And we mostly conclude that a campaign worked by looking at what the people we sent it to did next - which is not the same thing.\nThis role makes the company act per user, and know what that action was worth.\nYou own both halves of that: the models that decide who we aim at, and the causal reads that say whether it landed. Who is about to leave. Who will come back on their own without us spending anything. Whose decision a discount actually changes. And what a payer is worth over their life, once the discount, the refunds and the compute they consume are all inside the number.\nYou make sure:\nDiscounts and credit vouchers go to the people whose behaviour they change, and nowhere else\n\nWe know who is about to churn early enough to do something about it - and we know whether the something worked\n\nLifetime value is a number we can defend by cohort, by plan and by the discount a cohort was acquired on, not one blended average that hides all three\n\nEvery model and every campaign has a holdout behind it, so we can always state what it is worth in money\n\nWhat You Will Do\nChurn, retention & lifecycle propensity\nBuild churn and downgrade prediction for subscribers, and repeat-purchase propensity for one-time credit-pack buyers - two different machines, not one model with a flag on it.\n\nKnow the difference between a model that predicts churn and a model that reduces it. AUC earned by detecting users who already stopped using the product is worth nothing.\n\nHunt leakage relentlessly. Cancellation-adjacent features will hand you a beautiful offline number and a useless system.\n\nPair every score with an intervention and a holdout. The deliverable is a retained user, not a ranked list.\n\nUplift modelling & offer targeting\nOwn incrementality on everything we aim at a user: discounts, credit vouchers, trials, upgrade prompts, win-back campaigns, retention saves.\n\nModel uplift, not propensity. Targeting the users most likely to convert spends margin on people who were going to convert anyway; the entire value of this work is finding the users whose decision the offer changes.\n\nDesign the randomized holdouts that make uplift trainable and measurable in the first place, and keep them running permanently - including the ones marketing will ask you to switch off.\n\nRespect the guardrails. An offer model optimizing conversion alone will happily find the accounts that convert at negative gross margin, and it will find the abuse rings first. Margin and abuse signals are constraints on the objective, not a later cleanup.\n\nWork with Legal on what may be personalized. Personalized pricing and discounting touches consumer-protection and consent rules in several of our markets - you should want that conversation, not route around it.\n\nLifetime value & payer economics\nOwn LTV: cohort-based, censored honestly, stated on contribution margin rather than gross revenue, and split by plan, segment and acquisition discount.\n\nModel the parts that actually move it - refunds and chargebacks, credit breakage and expiry, plan migration and downgrades, and the compute an unlimited plan consumes.\n\nMake it a decision input, not a slide: what we can pay for a user, which discount depth pays back, which plan we should stop selling.\n\nKeep it current and versioned. A stale LTV curve is worse than no curve, because people act on it.\n\nExperimentation & causal measurement\nDesign and read the experiments behind pricing, packaging, offers and lifecycle - with the product analytics team, not in parallel to it.\n\nReach for the right tool when a clean experiment is impossible: difference-in-differences, matching, switchback and geo tests, synthetic control - and name the assumption each one is buying.\n\nSeparate \"users who do X retain better\" from \"making users do X improves retention.\" Every time, out loud.\n\nKill your own results. A finding that doesn't survive a second look should die at your hands, not in a board deck.\n\nHow we ship\nOwn the work end to end: framing the decision, features, training, evaluation, the scored audience landing in the tool that sends the offer, monitoring, retraining.\n\nModels ship as batch scores and policy rules into lifecycle tooling, the paywall and pricing - plus the readout that says what they earned. There is no real-time serving stack to babysit.\n\nMonitor for drift. New model launches, price changes, discount campaigns and seasonality all move the ground under a deployed score.\n\nWrite down what each model is worth, in money, and keep that number current.\n\nWho We're Looking For\nExperience shipping models that changed what the business did to real users - a targeted campaign, a pricing rule, a retention programme. Notebooks and offline benchmarks are not this.\n\nDepth in at least two of: churn and propensity modelling, uplift & causal ML, LTV and subscription economics, experimentation at scale.\n\nGenuine causal literacy: randomized holdouts, incrementality, Qini and uplift curves, selection effects, and why a lift measured before-and-after is usually not a lift.\n\nStrong SQL and Python: cohorts, funnels and retention on raw event data without help; gradient boosting; and the boring parts - feature pipelines, retraining cadence, a scoring job that runs on a schedule and doesn't rot.\n\nUnderstanding of subscription plus one-time purchase mechanics: recurring vs one-off revenue, refunds, plan changes, the deferred value of unspent credits, and why revenue and margin disagree about the same customer.\n\nCommercial judgment: you name the decision and the metric before you pick the model, and you know when the answer is a rule rather than a model.\n\nPragmatism. A segment rule shipped this month that lifts retention beats a two-quarter uplift platform. Then you replace the rule.\n\nClear written and spoken English, B2+.\n\nBackgrounds that often do well:\nGrowth, retention or monetization data scientists from subscription, gaming, fintech or e-commerce - anywhere offers and churn are modelled with real money attached\n\nCRM and lifecycle data scientists who owned campaign targetingandits incrementality\n\nCausal inference and uplift specialists who ship policies rather than papers\n\nStrong product analysts who moved into modelling and kept the decision instinct\n\nWhat This Role Is Not\nThis role is not a fit if you:\nWant to train or fine-tune generative video models - that's what our R&D ML Engineer roles do, and they're open\n\nWant to own a recommender or a serving stack - ranking is not in this role's scope today\n\nWould target an offer at the users most likely to buy and call it personalization\n\nReport a lift from a before-and-after comparison, or from comparing the users you targeted to everyone you didn't\n\nOptimize AUC and hand the score to someone else to work out what to do with it\n\nNeed clean labelled data, a feature store and an experimentation platform to exist before you can start\n\nWant predictable 9-5 workdays\n\nHiring process\nWe move fast:\nScreening call (30 min)\n\nTechnical interview & case study (1 hour)\n\nPractical home task (7 days)\n\nTeam interview (60 min)\n\nPaid on-site trial (1 month)\n\nWhat We Offer\nCompetitive base salary in USD, based on your experience, skills, and the scope of the role.\n\nEquity participation through the company’s stock option program, giving you the opportunity to share in Higgsfield’s long-term growth.\n\nRelocation support to Almaty for candidates moving from another city or country.\n\nA highly collaborative, fast-paced environment where you can work directly with experienced leaders and have a meaningful impact on the product and company.\n\nOpportunities for professional growth, ownership, and career development as the company 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