At a Glance
- Tasks: Lead advanced optimisation projects and enhance payment flows using data and ML services.
- Company: Join a tech powerhouse behind major digital experiences for brands like eBay and Spotify.
- Benefits: Flexible hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with mentorship opportunities and a focus on innovation.
- Why this job: Make a real impact on digital experiences while working with cutting-edge machine learning technologies.
- Qualifications: 5+ years in machine learning, strong Python skills, and experience with modern deployment practices.
The predicted salary is between 63000 - 77000 £ per year.
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.
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. 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 the 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.
Apply efficient data transformations using distributed computing (e. g., Mentor junior team members and utilise model explainability methods to drive feature performance improvements.
About You5+ years of experience in designing, building and maintaining machine learning models to solve complex, large-scale business problems. cluster or network effects).
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.
Proven experience designing and deploying recommender systems, contextual bandits, or network intelligence applications.
Experience with Docker, AWS/GCP, and modern ML deployment practices.
Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.
Data Science Data Science Senior Data Scientist (Remote) 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 Science Data Science Senior Data Scientist (Remote) in London
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Data Science Data Science Senior Data Scientist (Remote) 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.
