About Planet
Planet is a global provider of integrated technology and payments solutions for retail and hospitality customers.
We create great experiences for the millions of people who use our payments, software, and tax-free solutions every minute of every day.
Planet empowers its customers to deliver great customer experiences by combining payments and software in ways that drive greater loyalty, increase revenue and save time.
Founded over 35 years ago and with our headquarters in London, today we have more than 2,500 employees located across six continents serving our customers in more than 120 markets.
Role overview:
This is a long-term fixed-term contract designed to support Planet’s AI-first engineering scale-up project.
We're hiring a Data Scientist to join Planet's AI team and lead our commercial data science work. You'll partner with Sales, Marketing, Customer Success, and Finance to turn customer and revenue data into models and insights that directly move the top line — reducing churn, surfacing cross-sell opportunities, and giving the business a clear view of what's driving revenue.
This is a hands-on, high-impact role for someone who enjoys working close to the business, owns problems end-to-end, and can balance rigorous modelling with pragmatic delivery.
What you will do
- Churn modelling — Build, deploy, and iterate on customer churn prediction models. Identify leading indicators, segment at-risk cohorts, and work with Customer Success on retention playbooks.
- Cross-sell & upsell — Develop propensity models and next-best-action recommendations to help commercial teams prioritise accounts and product conversations.
- Revenue attribution & tracking — Design attribution frameworks across marketing channels, sales motions, and product touchpoints. Build the data products that let leadership see what's actually driving revenue.
- Experimentation — Design and analyse A/B tests and quasi-experiments for commercial initiatives. Bring statistical rigour to business decisions.
- Productionisation — Take models from notebook to production. Own the full lifecycle: feature engineering, training, deployment, monitoring, retraining.
- Stakeholder partnership — Translate ambiguous commercial questions into well-scoped analytical problems. Communicate findings clearly to non-technical audiences.
Who you are
- 5+ years in applied data science, with significant experience on commercial problems (churn, LTV, propensity, attribution, pricing, or similar).
- Strong SQL and Python (pandas, scikit-learn, statsmodels). Comfortable building production-grade code, not just notebooks.
- Hands-on experience with Snowflake (or a comparable cloud data warehouse — BigQuery, Redshift, Databricks).
- Experience deploying models on AWS (SageMaker, Lambda, ECS, or similar).
- Solid grounding in statistics, experimentation, and causal inference — you know when a correlation isn't enough.
- Track record of shipping models that are actually used by the business, not just built.
- Excellent communication — you can explain a model to a CFO and debug a feature pipeline with an engineer in the same afternoon.
Nice-to-have
- Experience with n8n or other workflow automation tools (Airflow, Prefect, Dagster).
- Exposure to payments, fintech, SaaS, or other recurring-revenue businesses.
- DBT experience for analytics engineering.
- Experience integrating LLMs or GenAI into commercial workflows.
Why Planet:
Planet is an equal opportunity employer where diversity is valued, and all employment is decided based on qualifications, merit, and business need.
Come and grow your career in the most exciting, fast paced technology market, with a business that delivers feel-good connected commerce.
We would love to hear from you – Apply now.
At Planet, we embrace a hybrid work model, with three days a week in the office.
Reasonable accommodations may be made in order to allow for an individual to perform the essential functions of this role successfully.
