At a Glance
- Tasks: Discover and design ML models to enhance payment performance for global merchants.
- Company: Join Checkout.com, a leading fintech powering digital experiences for major brands.
- Benefits: Flexible hybrid work model, competitive salary, and opportunities for personal growth.
- Other info: Dynamic environment with a focus on innovation and team success.
- Why this job: Make a real impact in fintech while collaborating with talented teams worldwide.
- Qualifications: 3+ years in machine learning, strong Python skills, and a passion for data-driven solutions.
The predicted salary is between 70000 - 90000 £ per year.
Company Description
We’re Checkout. com .
You might not know our name, but companies like e Bay, 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 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.
Key Responsibilities
- 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.
About You
- 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.
- Nice to have
- 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, Databrics, 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.
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 , follow us on Linked In and Instagram
- #J-18808-Ljbffr
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.
StudySmarter Expert Advice🤫
We think this is how you could land 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 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 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 Data Scientist in London
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.
