⌘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 Lead
Business Interview - 30min with VP Data
Case Study - Async assessment
Panel Interview - Case study Q&A
Final interview - Interview with Product Manager
