Time since we first saw it
Day 0
Early. Similar jobs usually come down between day 7 and day 57.
From 10,789 similar postings that came down. Coming down is not always a hire.
London, United Kingdom
Actively hiring, first seen by us todayWork setup in the employer’s posting: On-siteFrom the employer’s posting
Employment type in the employer’s posting: FullTimeFrom the employer’s posting
Checkout.com is looking for a Data Scientist to join our ambitious team, focused on discovering, designing, and experimenting with new estimators, models and features to boost payment performance across our portfolio of merchants.
Design and implement experiments to produce actionable insights, focusing on managing time-based data leakage and ensuring robust model evaluation.
Collaborate with other Data Scientists and engineers to productionise ML features, models and evolve our evaluation and monitoring frameworks.
Write high-quality, interpretable Python code for feature engineering and model training, contributing directly to our core products.
Checkout.com is looking for a Data Scientist to join our ambitious team, focused on discovering, designing, and experimenting with new estimators, models and features to boost payment performance across our portfolio of merchants. You will work closely with Data Scientists, Product and Engineering to enhance our core offering, protect customer lifetime value through network intelligence, and ensure safe model launches through robust observability.
Contribute to the research and development of new ML models and estimators to boost core Acceptance Rate performance. Design and implement experiments to produce actionable insights, focusing on managing time-based data leakage and ensuring robust model evaluation. Collaborate with other Data Scientists and engineers to productionise ML features, models and evolve our evaluation and monitoring frameworks.
Design and implement experiments to produce actionable insights, focusing on managing time-based data leakage and ensuring robust model evaluation. Collaborate with other Data Scientists and engineers to productionise ML features, models and evolve our evaluation and monitoring frameworks. Write high-quality, interpretable Python code for feature engineering and model training, contributing directly to our core products.
Collaborate with other Data Scientists and engineers to productionise ML features, models and evolve our evaluation and monitoring frameworks. Write high-quality, interpretable Python code for feature engineering and model training, contributing directly to our core products. Communicate hypotheses, evaluation results, and monitoring dashboards clearly to both technical and non-technical audiences.
3+ years of experience developing machine learning models to solve business problems.
Experience with advanced data transformation techniques (e.g., lambda functions).
Strong understanding of supervised ML algorithms, tuning, and performance evaluation.
Familiarity with, or hands-on experience in, recommender systems, contextual bandits, or network intelligence applications.
Experience with a range of feature engineering techniques (e.g. target encoding).
Experience in fintech, payments, or building cross-disciplinary relationships for advice and guidance.
Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 0
Early. Similar jobs usually come down between day 7 and day 57.
From 10,789 similar postings that came down. Coming down is not always a hire.
6 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordAdd your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Collaborate with other Data Scientists and engineers to productionise ML features, models and evolve our evaluation and monitoring frameworks. - Write high-quality, interpretable Python code for feature engineering and model training, contributing directly to our core products. - Communicate hypotheses, evaluation results, and monitoring dashboards clearly to both technical and non-technical audiences.
- Solid grasp of frequentist and Bayesian statistics for parameter estimation and experimentation. - Experience in writing clean, production-grade Python code for both model training and inference. - Excited to leverage LLMs for coding support and process optimisation to maximise personal and team productivity.
- Experience in fintech, payments, or building cross-disciplinary relationships for advice and guidance. - Familiarity with the unix shell, Databricks, Docker, and common cloud platforms (GCP/AWS). Additional Information
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
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New Data Scientist postings at the employers we follow are down 5% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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.
Checkout.com is looking for a Data Scientist to join our ambitious team, focused on discovering, designing, and experimenting with new estimators, models and features to boost payment performance across our portfolio of merchants. You will work closely with Data Scientists, Product and Engineering to enhance our core offering, protect customer lifetime value through network intelligence, and ensure safe model launches through robust observability.
Contribute to the research and development of new ML models and estimators to boost core Acceptance Rate performance.
Design and implement experiments to produce actionable insights, focusing on managing time-based data leakage and ensuring robust model evaluation.
Collaborate with other Data Scientists and engineers to productionise ML features, models and evolve our evaluation and monitoring frameworks.
Write high-quality, interpretable Python code for feature engineering and model training, contributing directly to our core products.
Communicate hypotheses, evaluation results, and monitoring dashboards clearly to both technical and non-technical audiences.
3+ years of experience developing machine learning models to solve business problems.
Strong understanding of supervised ML algorithms, tuning, and performance evaluation.
Experience with a range of feature engineering techniques (e.g. target encoding).
Solid grasp of frequentist and Bayesian statistics for parameter estimation and experimentation.
Experience in writing clean, production-grade Python code for both model training and inference.
Excited to leverage LLMs for coding support and process optimisation to maximise personal and team productivity.
Experience with advanced data transformation techniques (e.g., lambda functions).
Familiarity with, or hands-on experience in, recommender systems, contextual bandits, or network intelligence applications.
Experience in fintech, payments, or building cross-disciplinary relationships for advice and guidance.
Familiarity with the unix shell, Databricks, Docker, and common cloud platforms (GCP/AWS).
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.
Curious about what it’s like to be part of our team? Visit our Careers Page to learn more about our culture, open roles, and what drives us.
For a closer look at daily life at Checkout.com, follow us on LinkedIn and Instagram
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London, United Kingdom
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. Curious about what it’s like to be part of our team? Visit our Careers Page to learn more about our culture, open roles, and what drives us.
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