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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 experience 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.
The role involves 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 Edinburgh employer: Thyme
Contact Detail:
Thyme Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist in Edinburgh
✨Tip Number 1
Network like a pro! Reach out to people in the FinTech space, especially those working as Data Scientists. Use LinkedIn to connect and engage with them. A friendly chat can sometimes lead to job opportunities that aren't even advertised!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, especially those involving machine learning and AI. Share it on platforms like GitHub or your personal website. This gives potential employers a taste of what you can do!
✨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 work can impact real business decisions. Practice explaining complex concepts in simple terms – it’s key for communicating with non-technical stakeholders.
✨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 take the initiative to reach out directly. So, go ahead and submit your application today!
We think you need these skills to ace Data Scientist in Edinburgh
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, machine learning, and any relevant projects that showcase your skills in Python and AWS. We want to see how you can bring value to our team!
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 your background aligns with our mission at StudySmarter. Don’t forget to mention any specific experiences that relate to fintech or AI-driven initiatives.
Showcase Your Projects: If you've worked on any interesting data science projects, make sure to include them in your application. Whether it's a personal project or something from a previous job, we love seeing how you've tackled real-world problems using advanced analytics and machine learning.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about what we do at StudySmarter!
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're comfortable discussing your experience with Python libraries like NumPy, pandas, and scikit-learn. Prepare examples of ML models you've built and deployed, particularly using AWS services. This will demonstrate your hands-on expertise and readiness for the role.
✨Communicate Clearly
Practice explaining complex data insights in simple terms. You'll need to communicate with both technical and non-technical stakeholders, so being able to convey your findings clearly is crucial. Think of examples where you've successfully done this in past projects.
✨Prepare for Collaboration Questions
Since you'll be working closely with Product and Engineering teams, be ready to discuss your collaborative experiences. Think about how you've contributed to team projects and how you can help drive AI initiatives forward. Highlight your ability to work across different teams and influence strategy.
