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Data Scientist (ML) - Acceptance Rates

Company Description

Checkout.com is one of the most exciting fintechs in the world. Our mission is to enable businesses and their communities to thrive in the digital economy. We’re the strategic payments partner for some of the best known fast-moving brands globally such as Wise, Hut Group, Sony Electronics, Homebase, Henkel, Klarna and many others. Purpose-built with performance and scalability in mind, our flexible cloud-based payments platform helps global enterprises launch new products and create experiences customers love. And it's not just what we build that makes us different. It's how.

We empower passionate problem-solvers to collaborate, innovate and do their best work. That’s why we’re on the Forbes Cloud 100 list and a Great Place to Work accredited company. And we’re just getting started. We’re building diverse and inclusive teams around the world — because that’s how we create even better experiences for our merchants and our partners. And we need your help. Join us to build the digital economy of tomorrow.

Job Description

We're on the lookout for a mid-level Data Scientist to join our Acceptance Rates team, working on end-to-end research and development of Machine Learning models to optimise the payment performance of our merchants.

You'll be responsible for continually improving existing models and identifying new opportunities to apply Machine Learning to solve real world problems, using cutting-edge approaches such as Reinforcement Learning.

The work this team does has a proven track record of moving the needle within a product area that has high strategic importance to Checkout.com, so there's huge opportunity for tangible impact.

Key Responsibilities:

  • You will be expected to make substantial contributions to the research & development of new ML models

  • Design and implement experiments to produce actionable insights and improve model performance

  • Collaborate with other data scientists and engineers to productionise ML features/models

  • Write high-quality Python for feature engineering and model training

Qualifications

  • At least 2 years experience developing machine learning models to solve business problems

  • Strong understanding of: machine learning, probability and statistics

  • Experience applying scientific methods and thoughtful experimental design

  • Able to write high quality Python code

  • Experience with SQL databases

 

Nice to have

  • Experience in Financial Services/Fintech or Payments

  • Familiar with distributed general-purpose cluster-computing (e.g. Spark, Dask, Hadoop)

  • Experience with Docker.

  • Experience with AWS or at least another common cloud platform (GCP/Azure).

  • Familiar with the unix shell and shell scripting (for automating tasks).

Additional Information

Apply without meeting all requirements statement 

If you don't meet all the requirements but think you might still be right for the role, please apply anyway. We're always keen to speak to people who connect with our mission and values.

Hybrid Working Model: All of our offices globally are onsite 3 times per week (Tuesday, Wednesday, and Thursday). We’ve worked towards enabling teams to work collaboratively in the same space, while also being able to partner with colleagues globally. During your days at the office, we offer amazing snacks, breakfast, and lunch options in all of our locations.

We believe in equal opportunities

We work as one team. Wherever you come from. However you identify. And whichever payment method you use. 

Our clients come from all over the world — and so do we. Hiring hard-working people and giving them a community to thrive in is critical to our success.

When you join our team, we’ll empower you to unlock your potential so you can do your best work. We’d love to hear how you think you could make a difference here with us. 

We want to 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’ll be happy to support you.

Take a peek inside life at Checkout.com via

Apply without meeting all requirements statement 

If you don't meet all the requirements but think you might still be right for the role, please apply anyway. We're always keen to speak to people who connect with our mission and values.

We believe in equal opportunities

We work as one team. Wherever you come from. However you identify. And whichever payment method you use. 

Our clients come from all over the world — and so do we. Hiring hard-working people and giving them a community to thrive in is critical to our success.

When you join our team, we’ll empower you to unlock your potential so you can do your best work. We’d love to hear how you think you could make a difference here with us. 

We want to 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’ll be happy to support you.

Take a peek inside life at Checkout.com via

What You Should Know About Data Scientist (ML) - Acceptance Rates, Checkout.com

Are you ready to join Checkout.com as a Data Scientist (ML) focused on Acceptance Rates in the vibrant city of London? We’re a fintech powerhouse on a mission to redefine the digital economy, supporting brands like Wise and Sony Electronics. In this dynamic role, you’ll dive deep into the world of Machine Learning, working to enhance payment performance for our merchants. With a collaborative and innovative spirit, you’ll conduct research, design experiments, and develop cutting-edge ML models. Your contributions will directly impact our strategic goals and optimize real-world applications. We’re looking for someone with at least two years of experience in machine learning who is eager to engage with talented teams of data scientists and engineers. If you’re proficient in Python, SQL, and have a solid understanding of statistical methods, you’ll be an excellent fit for our team. Plus, if you have experience in the fintech sector or cloud platforms, that’ll give you an edge! Enjoy our hybrid working model where you'll collaborate in our London office three times a week while also enjoying flexibility. Join us and be part of a diverse community that celebrates inclusivity and innovation. Let’s shape the future together at Checkout.com!

Frequently Asked Questions (FAQs) for Data Scientist (ML) - Acceptance Rates Role at Checkout.com
What are the main responsibilities of a Data Scientist (ML) at Checkout.com?

As a Data Scientist (ML) at Checkout.com, your primary responsibilities will include developing new machine learning models to optimize payment performance, conducting research for actionable insights, collaborating with engineering teams to productionize models, and ensuring high standards in Python programming for feature engineering and training. You'll play a vital role in influencing strategies within a high-impact area.

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What qualifications are required for the Data Scientist (ML) position at Checkout.com?

To be considered for the Data Scientist (ML) role at Checkout.com, you should have a minimum of two years of experience in machine learning with a strong grasp of probability and statistics. Proficiency in Python and familiarity with SQL databases are essential. Experience in the fintech sector, along with knowledge of tools like Spark or AWS, is a plus but not mandatory.

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What kind of projects will a Data Scientist (ML) work on at Checkout.com?

In this role at Checkout.com, a Data Scientist (ML) will work on projects that involve optimizing machine learning models for payment performance and exploring new applications of ML methodologies. This could include innovative techniques like Reinforcement Learning, aimed at solving real-world issues affecting our merchants.

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Is experience in fintech necessary for the Data Scientist (ML) role at Checkout.com?

While not mandatory, having experience in the fintech industry can enhance your application for the Data Scientist (ML) role at Checkout.com. If you are familiar with payments systems or have worked in financial services, it will complement your technical skills and understanding of the industry needs.

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What is the work culture like at Checkout.com for a Data Scientist (ML)?

Checkout.com promotes a culture of collaboration and inclusiveness for its Data Scientist (ML) team. You will work in a hybrid environment, sharing ideas with a diverse team both in the office and remotely. The company encourages ongoing learning and enables you to push innovation, making it an exciting place to grow your career.

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Common Interview Questions for Data Scientist (ML) - Acceptance Rates
Can you describe your experience with developing machine learning models?

When answering this question, highlight specific projects where you've built or improved ML models. Discuss your methodology, tools you used, and how your contributions led to measurable outcomes. Focus on the real business problems you solved through your models.

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What programming languages are you proficient in, particularly for machine learning?

Be prepared to discuss your proficiency with Python, as it is a crucial requirement for the Data Scientist (ML) role. You should also mention any relevant libraries you are familiar with, like TensorFlow or Scikit-learn, and give examples of how you've applied them in past projects.

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How do you approach experimental design when working on ML projects?

Explain your process for designing experiments, emphasizing the importance of hypothesis formation, control variables, and statistical significance. Provide examples of how this structured approach has led to successful outcomes in your past projects.

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What are some key performance metrics you focus on for machine learning models?

Discuss various performance metrics relevant to ML, such as accuracy, precision, recall, and F1 score. Explain how you select metrics based on the project goals and the type of data you are working with.

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Can you give an example of a challenging machine learning problem you solved?

Choose a specific example that showcases your problem-solving skills. Explain the problem, your approach to tackle it, the techniques used, and the results achieved. This will illustrate your technical expertise and your ability to drive impact.

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What role does data cleaning play in your machine learning process?

Emphasize the importance of data quality in machine learning. Discuss your methods for data cleaning and preprocessing, and how they directly influence the performance of your models. Share any tools you commonly use for this purpose.

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How do you stay current with developments in machine learning technology?

Discuss your strategies for staying updated, such as following industry leaders on social media, participating in webinars, attending conferences, or enrolling in online courses. Highlight any relevant communities you belong to for networking and knowledge exchange.

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What experience do you have with deployment of ML models?

If applicable, share your experience of deploying ML models into production. Discuss any tools or platforms used (like AWS), the challenges faced during deployment, and how you ensured models maintained performance over time once deployed.

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Describe a time when you had to work collaboratively on a machine learning project.

Provide an instance where teamwork was essential. Talk about your role in the team, how you communicated with colleagues, and how collaboration contributed to the project's success. This shows your interpersonal skills and ability to work in a team environment.

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What are your long-term career goals as a Data Scientist?

Articulate your vision for growth within the data science field, indicating how you want to evolve with technology and contribute to the mission of the company, Checkout.com. This demonstrates your commitment and ambition in your career.

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Full-time, hybrid
DATE POSTED
January 4, 2025

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