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- Tasks: Transform complex data into insights and models that drive business decisions.
- Company: Established FinTech leader revolutionising global money management.
- Benefits: Competitive salary, 25 days holiday, private healthcare, and a learning budget.
- Why this job: Shape the future of AI and machine learning in a dynamic environment.
- Qualifications: Strong statistical modelling, advanced Python, and AWS skills required.
- Other info: Flexible hybrid working and excellent career growth opportunities.
The predicted salary is between 42000 - 84000 £ per year.
We’re working with an established FinTech / Payments business that has been helping customers manage and move money globally for many years. The company builds technology-led products that support low-cost, multi-currency payments and money management, operating across several regulated markets. They’re now investing further in their Data Science and AI capability and are looking for a Data Scientist to play a key role in shaping how advanced analytics, machine learning and AI are used across the business.
As a Data Scientist, you’ll work on turning complex datasets into meaningful insights and production-ready models that influence real business decisions. You’ll partner closely with Product, Engineering and Analytics teams, helping to identify where data science and machine learning can add the most value. This role combines hands-on technical work with the opportunity to influence strategy, tooling and ways of working, particularly around AI and ML adoption. You’ll be involved across the full lifecycle, from problem definition and experimentation through to deployment, governance and ongoing optimisation.
What you’ll be doing:
- Leading the use of advanced analytics, machine learning and AI within the data team
- Collaborating with Product and Engineering on strategic AI-driven initiatives
- Identifying and developing high-impact use cases for data science and ML
- Helping define ML lifecycle standards, documentation and governance
- Communicating insights and model outputs clearly to technical and non-technical stakeholders
What we’re looking for:
Essential experience:
- Strong grounding in statistical modelling, experimentation and inference
- Advanced Python skills (NumPy, pandas, scikit-learn, PyTorch or TensorFlow)
- Experience building, deploying and optimising ML models in production
- Strong AWS experience (e.g. SageMaker, Lambda or similar services)
- Expert SQL skills and experience working with large, complex datasets
- Solid data engineering fundamentals, including pipelines and APIs
- Comfortable with MLOps practices such as CI/CD, containerisation and monitoring
- Clear, pragmatic communicator who works well across teams
Nice to have:
- Experience with agentic or LLM-based frameworks
- Exposure to causal inference, uplift modelling or advanced experimentation
- Experience working in fintech or another regulated environment
- Awareness of data governance, privacy and model ethics
What’s on offer:
- Competitive salary with flexibility for the right profile
- 25 days holiday plus an additional day off
- Annual learning and development budget
- Private healthcare and wellbeing support
- Pension, life assurance and additional benefits
- Hybrid working with flexibility where possible
This role would suit someone who enjoys working on real-world data problems, wants to influence how AI and machine learning are used responsibly in production, and is looking for a role with both technical depth and business impact. If you’re interested, apply directly or reach out for a confidential conversation.
Data Scientist in London employer: Thyme
Contact Detail:
Thyme Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist in London
✨Tip Number 1
Network like a pro! Reach out to people in the FinTech space, especially those working with data science. Use platforms like LinkedIn to connect and engage with them. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, especially those involving machine learning and AI. This will give potential employers a taste of what you can do and how you can add value to their team.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the business side of things. Be ready to discuss how your experience with Python, SQL, and AWS can help the company achieve its goals. We want to see you shine!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search. Let’s get you that Data Scientist role!
We think you need these skills to ace Data Scientist in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Scientist role. Highlight your experience with statistical modelling, Python, and machine learning. We want to see how your skills align with what we’re looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about data science and how you can contribute to our team. Don’t forget to mention any relevant projects or experiences that showcase your skills.
Showcase Your Projects: If you’ve worked on any interesting data science projects, make sure to include them in your application. We love seeing real-world applications of your skills, especially if they involve advanced analytics or machine learning!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at Thyme
✨Know Your Data Science Fundamentals
Brush up on your statistical modelling and machine learning concepts. Be ready to discuss how you've applied these in real-world scenarios, especially in fintech or regulated environments. This will show that you not only understand the theory but can also implement it effectively.
✨Showcase Your Technical Skills
Make sure you can demonstrate your advanced Python skills and experience with libraries like NumPy and pandas. Prepare to talk about specific projects where you've built, deployed, and optimised ML models, particularly using AWS services like SageMaker.
✨Communicate Clearly
Practice explaining complex data insights in simple terms. You’ll need to communicate effectively with both technical and non-technical stakeholders, so think of examples where you’ve successfully done this in the past.
✨Prepare for Collaboration Questions
Since the role involves working closely with Product and Engineering teams, be ready to discuss your experience in cross-functional collaboration. Think of instances where your input influenced strategy or tooling decisions, and how you navigated any challenges.
