Data Scientist
# Data Scientist **Timeleft** · `Remote` · `Full Time` 💼 **Уровень роли:** `Mid-level` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Data-Scientist]` `[Machine-Learning-Engineer]` `[Applied-Scientist]` `[Data-Science]` `[Search-Data-Scientist]` `[AI-Data-Scientist]` `[Data-Science-Expert]` `[Data-Science-Specialist]` `[ML-Data-Scientist]` `[Statistical-Data-Scientist]` `[Research-Data-Scientist]` `[Data-Scientist-Analytics]` --- ### About the Role ⌘Role Overview The Data Scientist is the first hire on the team whose job is to put machine learning into production , not just into a notebook. You'll build models that power product and other live flows sometimes surfaced to users in real time, not just reported on in a dashboard a week later. The first flagship project is about personalization across the user journey: a model that decides, per user, what offer to show, wired directly into the product and lifecycle experience rather than sitting in a warehouse table. From there, you'll extend the same muscle: model → API → product surface to other high-leverage moments in the user journey. You'll work hand-in-hand with Product, Engineering and Lifecycle marketing to ship models as features, not as reports. This is also a foundational role for the team's infrastructure: most of what Data does today is batch (dbt, Lightdash, BigQuery); you'll help establish our first real-time low-latency serving patterns on GCP and work closely with engineering on this. ⌘Key Responsibilities 1. Production ML Development - Build, validate, and ship ML models (propensity, pricing/discount optimization, personalization, churn/LTV) that go live in the product, not just proof-of-concept notebooks. - Own the full lifecycle: problem framing, feature engineering, training, evaluation, deployment, monitoring, retraining. - Write production-grade code (tested, versioned, reviewed) — you'll be shipping alongside Engineering, held to their bar. 2. Personalization across the journey: from paywall to lifecycle - Design and ship the personalized discounting model: who gets what offer, and why, served at the moment of the paywall decision. - Partner with product on machine learning experiment design (A/B, holdouts) to prove causal lift of the models, not just correlation. - Build the measurement framework so pricing/discount decisions are defensible to finance and leadership. 3. ML Infrastructure & MLOps (GCP) - Stand up our first low-latency model serving pattern on GCP (e.g., Vertex AI endpoints, Cloud Run, or equivalent) - Define the feature pipeline pattern: what's precomputed in BigQuery/dbt vs. what needs to be fresh/real-time via Pub/Sub or similar. - Set up model monitoring: drift, staleness, prediction quality so a live model doesn't silently degrade. 4. Product & Engineering Partnership - Sit close to Product and Engineering, not just Data this role is measured by what ships to actual users, not just notebooks - Translate a product problem ("how might we reactive lapsed payers") into a modeling problem, and a model output into an API contract Engineering can build against. - Document handoffs clearly enough that Engineering can own the serving layer long-term without you as a bottleneck. 5. Experimentation & Causal Inference - Design uplift/causal models where "who responds to a discount" matters more than "who churns" . - Run and interpret experiments that isolate the model's actual incremental impact on revenue/retention. - Design the experimentation program for improving data science and machine learning models with the same rigour we use across our already ongoing experimentation programs ⌘Expected Outcomes - Personalized discounting model live in production , serving real paywall decisions to real users: shipped end-to-end, not a prototype sitting in staging. - A documented, reusable low-latency serving pattern established on GCP (Vertex AI endpoints or Cloud Run) — the next model doesn't require rebuilding this from scratch. - Proven incremental lift on a core business metric (paywall conversion, discount margin efficiency, or lapsed-payer reactivation — pick the one you want as the flagship KPI), demonstrated through a controlled experiment, not just before/after comparison. - Model monitoring in place — drift and staleness alerts mean the team knows within days, not months, if a live model silently degrades. - A repeatable model-to-production playbook that others in the team can follow ⌘Skills & Competencies Must have (hard skills) - Strong Python for data science and ML (scikit-learn, XGBoost/LightGBM; PyTorch or TensorFlow a plus if deep learning is relevant to future use cases). - Proven track record shipping models to production, not just modeling in a notebook. Can talk through at least one model that served live traffic. - Hands-on experience with a cloud ML platform, ideally GCP (Vertex AI, BigQuery ML, Cloud Run/Functions) or fast ability to translate equivalent AWS/Azure experience. - Solid SQL; comfortable working against a dbt/BigQuery warehouse. - Software engineering fundamentals: git, code review, testing, CI/CD: You'll be shipping code Engineering has to trust. - Causal inference / uplift modelling or applied experimentation experience: pricing and discounting need "what if we hadn't," not just "who churns." Nice to have - Experience with streaming/event pipelines (Pub/Sub, Dataflow, Kafka): useful as we move off pure batch. - Experience with pricing, discounting, personalization specifically. - Familiarity with feature stores or the DIY equivalent (versioned feature pipelines). - Multi-armed bandits or reinforcement learning for pricing/personalization. - Startup experience: comfortable being the first person to build something rather than joining an existing ML platform team. Soft skills - Genuinely energised by "does this move the metric," not just "is this model accurate." - Can hold their own in a room with Engineering and with Business: Speaks commercial as well as the language of engineering - Explains modeling tradeoffs in plain business terms to Product/leadership without dumbing it down or using too much jargon - Comfortable owning ambiguity — this role is defining the pattern, not following one. ⌘ Required experience - 4–7 years in a data scientist / ML engineer role, with at least one model you personally took from prototype to live production serving real users or real traffic. - Quantitative background (CS, stats, engineering or equivalent hands-on experience). - B2C, subscription, or marketplace experience is a strong plus - Fluent English. ⌘ Recruitment process Introduction Call - 30min with Talent Acquisition Manager Business Interview - 30min with VP Data Case Study - Async assessment Panel Interview - Case study Q&A Final interview - Interview with Product Manager Originally posted on Himalayas
- Data-Scientist
- Machine-Learning-Engineer
- Applied-Scientist
- Data-Science
- Search-Data-Scientist
- AI-Data-Scientist
- Data-Science-Expert
- Data-Science-Specialist
- ML-Data-Scientist
- Statistical-Data-Scientist
- Research-Data-Scientist
- Data-Scientist-Analytics
Наблюдалась 2026-10-05, впервые 2026-10-05, источник — Himalayas (JSON API).