Data Scientist

About this 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.