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Checkout.com

Senior Data Scientist

London
Posted about 22 hours ago
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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.

About The Role

Checkout.com is looking for an experienced Senior Data Scientist to lead advanced optimisation projects and champion the development of robust, sustainable ML architectures. You will be responsible for enhancing the payment flow through data and ML services, advancing the complex problem of multi-objective optimisation. You will work closely with other Senior and Staff Data Scientists to shape the architecture of our decisioning systems.

Key Responsibilities

  • Lead projects and architecture design for multi-objective optimisation and scalable models.
  • Create custom loss functions, evaluation, and tuning frameworks that reflect complex business problems.
  • Collaborate with product stakeholders to align technical strategy with business goals and resolve blockers across the team.
  • Apply efficient data transformations using distributed computing (e.g., Spark, Dask) and ensure robust test coverage for production systems.
  • Mentor junior team members and utilise model explainability methods to drive feature performance improvements.

Reasons to use Rodeo

I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?

Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.

Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.

Start with a chat, not a search bar

Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.

P

Graduate Consultant — 2026 Scheme

PwC·London, UK
£35,000/yr

Why you're a good match

Strong

Your economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.

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It searches the market for you

Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.

Why you're a good match

You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.

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Strong

Experience fit

Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.

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Strong

Only hits

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About You

  • 5+ years of experience in designing, building and maintaining machine learning models to solve complex, large-scale business problems.
  • Deep understanding of frequentist and Bayesian statistics, plus supervised and unsupervised modelling techniques.
  • Proven experience modelling complex interactions (e.g., cluster or network effects).
  • Expertise in model explainability to tune feature engineering and understand feature interactions.
  • Ability to write high-quality, production-grade Python code and collaborate effectively with non-technical stakeholders.
  • Proficient in leveraging LLMs for coding support and process optimisation to maximise personal and team productivity.
  • Adept at tuning technical communication for broad audiences.
  • Proven ability to build trusting relationships with product stakeholders and deeply understand business models.

Nice to have

  • Experience with distributed general-purpose cluster-computing.
  • Proven experience designing and deploying recommender systems, contextual bandits, or network intelligence applications.
  • Experience in fintech, payments or similar domains.
  • Experience with Docker, AWS/GCP, and modern ML deployment practices.

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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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Jessica, London

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Skills

Machine Learning
Multi-objective Optimisation
Python
Statistics
Bayesian Statistics
Frequentist Statistics
Distributed Computing
Spark
Dask
Model Explainability
LLMs
Docker
AWS
GCP
Recommender Systems
Contextual Bandits

Location

London, England, United Kingdom

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