[Hiring] Data Scientist @Timeleft

Role Description

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.
• 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.

Qualifications

• 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.
• 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.
• Causal inference / uplift modelling or applied experimentation experience.

Requirements

• 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.

Benefits

• Personalized discounting model live in production, serving real paywall decisions to real users.
• A documented, reusable low-latency serving pattern established on GCP.
• Proven incremental lift on a core business metric demonstrated through a controlled experiment.
• Model monitoring in place — drift and staleness alerts.
• A repeatable model-to-production playbook that others in the team can follow.

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