Senior Data Scientist in London

Senior Data Scientist in London

London Full-Time 70000 - 90000 £ / year (est.) Home office (partial)
Checkout.com

At a Glance

  • Tasks: Lead advanced optimisation projects and develop robust ML architectures.
  • Company: Join Checkout.com, powering payments for brands like eBay and Spotify.
  • Benefits: Flexible hybrid work model, competitive salary, and opportunities for personal growth.
  • Other info: Collaborative culture with a focus on mentorship and career development.
  • Why this job: Make a real impact in fintech while working with cutting-edge technology.
  • Qualifications: 5+ years in machine learning, strong coding skills, and experience with complex business problems.

The predicted salary is between 70000 - 90000 £ per year.

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. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale.

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 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.
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.

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.

Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.

Senior Data Scientist in London employer: Checkout.com

Checkout.com is an exceptional employer that champions a flexible hybrid working model, allowing employees to balance their professional and personal lives effectively. With a strong emphasis on growth and collaboration, the company provides ample opportunities for career development while working alongside talented teams in the dynamic financial services sector in London. Joining Checkout.com means being part of a forward-thinking organisation that values compliance and innovation in payments and product regulation.

Checkout.com

Contact Details:

Checkout.com Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Senior Data Scientist in London

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 Checkout.com!

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 Senior Data Scientist at Checkout.com.

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 Checkout.com.

Apply Directly through Our Website

When you find a suitable opening like Senior Data Scientist at Checkout.com, 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 Senior Data Scientist in London

Machine Learning
Multi-Objective Optimisation
Custom Loss Functions
Data Transformation
Distributed Computing (e.g., Spark, Dask)
Model Explainability
Python Programming

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 Checkout.com, 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 Checkout.com. 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 Checkout.com

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 Checkout.com!

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.