Uplift modelling & personalized offers

• Own incrementality on offers: discounts, credit vouchers, trials, plan upgrade prompts, win-back campaigns.
• Model uplift, not propensity. Targeting the users most likely to convert spends margin on people who would have converted anyway; the entire value of this work is finding the users whose decision the offer changes.
• Design the randomized holdouts that make uplift trainable and measurable in the first place, and keep them running permanently.
• Respect the guardrails: an offer model optimizing conversion alone will happily find the accounts that convert at negative gross margin, and it will find abuse rings first. Margin and abuse signals are constraints on the objective, not a later cleanup.
• Work 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.

Churn, retention & lifecycle propensity

• Build churn and downgrade prediction for subscribers, and repeat-purchase propensity for credit-pack buyers.
• Know the difference between a model that predicts churn and a model that reduces it. A high AUC earned by detecting users who already stopped using the product is worth nothing.
• Watch for leakage relentlessly - cancellation-adjacent features will hand you a beautiful offline number and a useless system.
• Pair every propensity model with an intervention and an experiment. The deliverable is a retained user, not a score.

Generation intelligence (the feature layer)

• Turn what users actually make into features the models above can use: use-case and intent labels over prompts, input assets, output assets, model and parameters.
• Content understanding here is instrumental — a taxonomy exists so ranking knows what a preset is for and so an offer knows which use case a user is stuck on. Multimodal, because a large share of our generations carry almost no prompt text and the intent lives in the uploaded image.
• Mine failed, abandoned and refunded generations: they are the strongest churn and unmet-demand signal we have, and success-only training data has survivorship bias built in.

How we ship

• Own models end to end: problem framing, features, training, offline evaluation, serving, monitoring, retraining.
• Ship behind experiments. Offline metrics decide what to try; online experiments decide what stays.
• Monitor for drift and degradation - new model launches, pricing changes and seasonality all move the ground under a deployed model.
• Write down what each model is worth, in money, and keep that number current.

Who We're Looking For

Experience shipping ML that served live user traffic and changed a business metric. Notebooks and offline benchmarks are not this.

• Depth in at least two of: recommender systems / learning-to-rank, uplift & causal ML, churn or propensity modelling, real-time personalization.
• Genuine causal literacy: randomized holdouts, incrementality, Qini/uplift evaluation, selection effects, why a lift measured before-and-after is usually not a lift.
• Strong Python and SQL; comfortable with gradient boosting and neural ranking, and with the boring parts - feature pipelines, training/serving skew, latency budgets, retraining cadence.
• Product judgment: you can name the decision and the metric before you pick the model, and you know when the answer is a rule rather than a model.
• Comfort with images and video as data, or clear appetite to get there fast.
• Pragmatism. A heuristic shipped this month that lifts conversion beats a two-quarter platform. Then you replace the heuristic.
• Clear written and spoken English, B2+.

What We Offer

• Competitive base salary in USD, based on your experience, skills, and the scope of the role.
• Equity participation through the company’s stock option program.
• Relocation support to Almaty for candidates moving from another city or country.
• A highly collaborative, fast-paced environment where you can work directly with experienced leaders.
• Opportunities for professional growth, ownership, and career development.
• Company-provided equipment, meals, transportation, or other office benefits.

This is a fully on-site role based in our Almaty office. Our team works from the office five days per week for the full working day.