Product Data Scientist London

Checkout Ltd; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Job summary

Checkout.com, headquartered in London, is seeking a Product Data Scientist to drive analytics for Platforms & SMB, shaping onboarding journeys and predictive automation.

You will partner with product managers, engineers, and analytics teams to measure success, log essential data, and deliver actionable insights that influence product strategy and performance.

Qualifications

• Experience in data science or product analytics roles within a high-growth tech environment.
• Strong SQL ability and Python proficiency for data analyses.
• Foundational ML knowledge plus understanding of statistics concepts.
• Analytical mindset and ability to translate operational problems into data-informed solutions.
• Clear and precise communication of data insights to non-technical stakeholders.

Responsibilities

• Build experiments and analysis frameworks to measure operational efficiency and ROI of new software releases for Platforms & SMB.
• Collaborate with Data Analytics Engineers and Software Engineers to log and model the right data for high-integrity insights.
• Design and run PoC machine learning models and heuristic solutions to evaluate onboarding automation feasibility before handoff.

Skills

SQL

Python

Data analysis

Statistics

NLP basics

Job description

Company Description

We’re Checkout.com. You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day.

We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers.

Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale.

If you want to do career-defining work, you’ve come to the right place. We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact.

With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started.

As a Product Data Scientist, you'll work as part of a cross-functional team alongside product managers, designers, and software and analytics engineers, using data and your analytical expertise to influence the strategy of our Activation, Risk and Configuration products. You'll focus on Platforms and SMB domain where you will help optimize the onboarding journey for merchants and scale predictive automation.

You'll help define how we measure the success of our products, collaborate with engineers on data collection, build analytical frameworks, and run insights to find product improvement opportunities.

Data Analytics at Checkout.com is a highly visible function that critically impacts the company’s success. As part of this group, you'll have a strong support network of Senior Data Scientists, Analytics Engineers, and Data Product Managers to help you develop your technical and data science practice.

How You’ll Make an Impact

• Efficiency & Product Measurement: Build experiments and analysis frameworks to measure the operational efficiency and ROI of new software releases and internal tooling updates for the Platforms & SMB domain.

• Data Partnerships: Work closely with Data Analytics Engineers and Software Engineers to ensure we log and model the right data to produce high-integrity business insights.

• Data-driven automations and solutions:

  • Apply statistical modeling and exploratory data analysis to identify bottlenecks and drop-off points across the merchant onboarding journey, delivering insights to help Product Managers prioritize automation initiatives.

  • Design and run proof-of-concept (PoC) machine learning models and heuristic solutions to evaluate the feasibility of proposed onboarding automations before engineering handoff for implementation.

What We’re Looking For

• Experience: Proven experience in similar data science or product analytics roles and in a high-growth tech environment

• Technical foundations: Excellent data interrogation skills with SQL and the ability to comfortably write, read, and iterate through Python scripts to run data science analyses.

• Foundational ML knowledge: A good understanding of foundational data science concepts (e.g., statistics, clustering, basic NLP workflows). You do not need experience deploying ML models to production, but you should understand how to apply them to data tasks.

• Analytical Mindset: A strong analytical mind with a demonstrable ability to take operational problems and convert them into structured, data-informed solutions.

• Communication: Clear and precise communicator, able to explain data insights and analytical logic to non-technical stakeholders (Product Managers and Operations teams).

Additional Information

Bring all of you to work

We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one.

Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity, and where your growth is in your hands.

We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here.

It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable.

Life at Checkout.com

We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.

For a closer look at daily life at Checkout.com, follow us on LinkedIn and Instagram.