At a Glance
- Tasks: Lead data science projects to drive revenue and reduce churn using advanced modelling techniques.
- Company: Join Planet, a global leader in tech and payments solutions with a vibrant culture.
- Benefits: Enjoy competitive pay, hybrid work model, and opportunities for professional growth.
- Other info: Be part of a diverse team in a fast-paced tech environment with excellent career prospects.
- Why this job: Make a real impact by turning data into actionable insights that boost business success.
- Qualifications: 5+ years in data science, strong SQL and Python skills, and experience with cloud platforms.
The predicted salary is between 56700 - 69300 £ per year.
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:
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. 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.
Data Scientist employer: Planet Payment
At Planet, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. Our commitment to employee growth is evident through our diverse opportunities for professional development, particularly in the fast-paced technology sector. With our headquarters in London, we provide a hybrid work model that balances office engagement with flexibility, ensuring our team members thrive both personally and professionally.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Planet Payment!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data Scientist at Planet Payment.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Planet Payment.
✨Apply Directly through Our Website
When you find a suitable opening like Data Scientist at Planet Payment, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data Scientist
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Planet Payment, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Planet Payment. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Planet Payment
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Planet Payment!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
