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- Tasks: Develop and validate models for analysing non-performing loan portfolios using cutting-edge technology.
- Company: A growing fintech platform backed by a global investment firm.
- Benefits: Competitive salary, innovative work environment, and opportunities for professional growth.
- Why this job: Join a dynamic team and make impactful decisions in the fintech space.
- Qualifications: Degree in STEM, strong skills in statistics, machine learning, and Python.
- Other info: Build an analytics function from scratch and work with massive datasets.
The predicted salary is between 36000 - 60000 £ per year.
We are working with a growing consumer finance fintech platform, who are looking to build out their Data and Analytics department. The team, who have recently received backing from a global Investment firm, are now building out a Data and Analytics team in London - with a focus on supporting their Unsecured NPL Portfolio Investment team.
This role is ideal for a technically strong, quantitatively minded professional with a background in statistics and data science, looking to apply their skills in financial services. While specific NPL experience is not required, a passion for large-scale data analysis and modern analytics frameworks is essential.
You will work in a neo-bank/fintech-style environment, using cutting-edge technology to analyze terabytes of data and support high-impact investment decisions.
Responsibilities:- Develop, implement, and validate statistical and machine learning models for analysing non-performing loan portfolios.
- Collaborate with cross-functional teams—including credit, collections, and data engineering—to translate business objectives into robust analytical solutions.
- Build software using modern technology to enable investing and asset management at scale.
- Apply Bayesian modelling and probabilistic programming techniques to address uncertainty and improve prediction accuracy.
- Analyse large-scale datasets to identify key drivers, trends, and early warning signals within NPL portfolios.
- Clearly communicate model results, insights, and recommendations to stakeholders, including both technical and non-technical audiences.
- Stay current with advances in statistical modelling, machine learning, and data science, continuously evaluating and integrating new techniques and tools.
- University degree in a STEM field (e.g., Mathematics, Statistics, Computer Science, Engineering, Physics, Economics); advanced degree preferred.
- Strong expertise in statistical modelling, Bayesian inference, and machine learning.
- Proficient in Python (using libraries such as NumPy, pandas, scikit-learn, PyMC or Stan).
- Experienced in SQL. Ability to write efficient and robust queries.
- Demonstrated experience working with large and complex datasets.
- Ability to communicate complex analytical concepts clearly and effectively to a range of audiences.
- Experience with model governance, documentation, and deployment best practices.
- Experience with cloud environments (e.g., AWS Sagemaker).
- Experience with collaborative development tools (e.g., Git, JIRA) is a plus.
- Prior experience in financial services, banking, or credit risk modelling is beneficial.
- 5-8 years experience in a Data/Analytics role, ideally within a Financial Institution.
- Opportunity to build an analytics function from the ground up in a cutting-edge, entrepreneurial environment.
- Work with massive datasets and modern tools at the forefront of fintech innovation.
- High-impact role directly contributing to investment and portfolio decision-making.
Data Scientist - Investments and FinTech in London employer: Anonymous
Contact Detail:
Anonymous Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - Investments and FinTech in London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the fintech and data science space. Attend meetups, webinars, or even just grab a coffee with someone in the industry. 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 analysis projects, especially those that relate to finance or investments. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in data science and fintech. Be ready to discuss how you can apply your knowledge to their specific challenges, especially around non-performing loans.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it’s super easy to keep track of your applications that way!
We think you need these skills to ace Data Scientist - Investments and FinTech in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Data Scientist in Investments and FinTech. Highlight your experience with statistical modelling, machine learning, and any relevant projects that showcase your skills in analysing large datasets.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to express your passion for fintech and data analysis. Mention specific experiences that align with the responsibilities listed in the job description, and don’t forget to show your enthusiasm for working in a neo-bank environment.
Showcase Your Technical Skills: Since this role requires strong technical expertise, make sure to list your proficiency in Python, SQL, and any other relevant tools or frameworks. If you’ve worked with Bayesian modelling or cloud environments, be sure to highlight that too!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity in our growing Data and Analytics team!
How to prepare for a job interview at Anonymous
✨Know Your Data Science Stuff
Make sure you brush up on your statistical modelling and machine learning techniques. Be ready to discuss how you've applied these skills in real-world scenarios, especially in financial contexts. Practising coding in Python and SQL will also help you feel more confident during technical questions.
✨Understand the FinTech Landscape
Familiarise yourself with the latest trends in fintech, particularly around consumer finance and non-performing loans (NPLs). Showing that you have a genuine interest in the industry and can discuss current challenges and innovations will impress your interviewers.
✨Prepare for Collaboration Questions
Since this role involves working with cross-functional teams, think of examples where you've successfully collaborated with others. Be ready to explain how you translated complex data insights into actionable strategies for both technical and non-technical stakeholders.
✨Showcase Your Problem-Solving Skills
Be prepared to tackle hypothetical scenarios or case studies during the interview. Think about how you would approach analysing large datasets or developing models for NPL portfolios. This is your chance to demonstrate your analytical thinking and creativity in problem-solving.
