At a Glance
- Tasks: Lead end-to-end machine learning solutions in a regulated finance environment.
- Company: Fast-growing UK business focused on data innovation.
- Benefits: Competitive salary, additional paid leave, and employee assistance programme.
- Other info: Collaborative environment with opportunities for mentorship and career growth.
- Why this job: Make a real impact by driving ML solutions that enhance financial services.
- Qualifications: Proven experience in ML delivery, Python, SQL, and AWS.
The predicted salary is between 80000 - 100000 £ per year.
My client is a fast-growing UK business serving thousands of customers. They are investing heavily in their data capability and are now looking to appoint a Lead Data Scientist to drive end-to-end machine learning delivery within a regulated financial environment. This is a hands-on role combining technical ownership and production-grade model deployment.
The Role
- Own end-to-end ML solutions - from problem framing and feature engineering to deployment, monitoring, and governance
- Translate business objectives into modelling strategies aligned to risk appetite and operational constraints
- Build and deploy models using Python, SQL, and AWS (SageMaker or equivalent)
- Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions
- Establish monitoring frameworks for performance, drift, and retraining
- Drive clear documentation, traceability, and governance appropriate for a regulated environment
This role requires someone who thinks beyond experimentation - focusing on operational impact, adoption, and long-term model performance.
Requirements
- Proven commercial ML/Data Science delivery with measurable impact
- Experience taking models into production and managing performance over time
- Prior experience leading or mentoring Data Scientists
- Strong Python (pandas, numpy, scikit-learn or similar)
- Strong SQL (complex joins, aggregations, analytical functions)
- Experience working closely with Engineering/Data teams in production-first environments
- Comfortable operating within regulated industries
- AWS experience (S3, Athena/Glue, IAM, Lambda)
- SageMaker or equivalent ML platform experience
- Financial services domain knowledge (risk, fraud, affordability, payments)
- Experience with model explainability and governance documentation
Additional paid leave (birthday, charity, wellbeing, life events) Employee assistance programme & Virtual GP
Lead Data Scientist: End-to-End ML for Regulated Finance in Manchester employer: Adria Solutions Ltd
As a fast-growing UK business, we pride ourselves on fostering a dynamic work culture that prioritises innovation and collaboration. Our commitment to employee growth is evident through our investment in data capabilities and the opportunity for hands-on experience in a regulated financial environment. With unique benefits such as additional paid leave for special occasions and a supportive employee assistance programme, we offer a rewarding workplace for those looking to make a meaningful impact in the field of data science.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Scientist: End-to-End ML for Regulated Finance in Manchester
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your end-to-end ML projects, especially those relevant to regulated finance. This will give potential employers a taste of what you can do and how you think.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with Python, SQL, and AWS, and how you've tackled challenges in previous roles.
✨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.
We think you need these skills to ace Lead Data Scientist: End-to-End ML for Regulated Finance in Manchester
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Lead Data Scientist. Highlight your experience with end-to-end ML solutions and any relevant projects you've worked on in regulated environments. We want to see how your skills align with our needs!
Showcase Your Technical Skills:Don’t hold back on your technical prowess! Be specific about your experience with Python, SQL, and AWS. Mention any tools or frameworks you’ve used, especially SageMaker, as this will catch our eye and show you’re ready for the hands-on nature of the role.
Demonstrate Impact:We love seeing measurable impact from your previous work. Share examples of how your models have driven operational success or improved performance over time. This will help us understand your ability to think beyond experimentation.
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 keep track of your application and ensure it gets the attention it deserves. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Adria Solutions Ltd
✨Know Your ML Inside Out
Make sure you’re well-versed in end-to-end machine learning processes. Be ready to discuss everything from problem framing to deployment and monitoring. Brush up on your Python, SQL, and AWS skills, as you'll likely be asked to demonstrate your technical prowess.
✨Understand the Regulatory Landscape
Since this role is within a regulated financial environment, it’s crucial to understand the compliance aspects of machine learning. Familiarise yourself with governance documentation and model explainability, as these will be key topics during your interview.
✨Showcase Your Collaboration Skills
This position requires close partnership with various teams like Engineering and Risk. Prepare examples of how you've successfully collaborated in the past, especially in production-first environments. Highlight your experience in mentoring others, as leadership is a big part of this role.
✨Prepare for Scenario-Based Questions
Expect scenario-based questions that assess your problem-solving abilities in real-world situations. Think about challenges you've faced in previous roles, particularly around model performance and operational impact, and be ready to discuss how you tackled them.
