Data Scientist

• 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

Source: Himalayas