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For Companies At a Glance
- Tasks: Lead end-to-end machine learning solutions in a dynamic financial environment.
- Company: Fast-growing UK business focused on data innovation.
- Benefits: Hybrid working, competitive pension, extra paid leave, and employee support programmes.
- Other info: Join a modern office with excellent career growth opportunities.
- Why this job: Make a real impact with your data skills in a collaborative team.
- Qualifications: Proven ML experience, strong Python and SQL skills, and leadership abilities.
The predicted salary is between 60000 - 80000 £ 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.
Essential Experience
- 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)
- Solid grounding in applied statistics, evaluation design, calibration, bias/fairness
- Experience working closely with Engineering/Data teams in production-first environments
- Comfortable operating within regulated industries
Desirable
- 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
Package & Benefits
- Hybrid working model
- Competitive pension
- Additional paid leave (birthday, charity, wellbeing, life events)
- Employee assistance programme & Virtual GP
- Modern collaborative office environment
Interested?
Data Science Data Science Senior Data Scientist (Remote) employer: Adria Solutions Ltd
Contact Detail:
Adria Solutions Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Science Data Science Senior Data Scientist (Remote)
✨Tip Number 1
Network like a pro! Reach out to your connections in the data science field, especially those who work in financial services. A friendly chat can lead to insider info about job openings or even a referral.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best machine learning projects. Use platforms like GitHub to share your code and document your process. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills. Be ready to discuss your experience with Python, SQL, and AWS. Practise explaining complex concepts in simple terms – it shows you can communicate effectively with non-technical teams.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Data Science Data Science Senior Data Scientist (Remote)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Senior Data Scientist. Highlight your experience with end-to-end ML solutions and any relevant projects that showcase your skills in Python, SQL, and AWS.
Showcase Your Impact: When detailing your previous roles, focus on the measurable impact you've had. Use specific examples of how your work has driven operational success or improved model performance in a regulated environment.
Be Clear and Concise: In your cover letter, get straight to the point. Clearly outline how your experience aligns with the job requirements and why you’re excited about the opportunity to work with us at StudySmarter.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates!
How to prepare for a job interview at Adria Solutions Ltd
✨Know Your ML Inside Out
Make sure you can discuss your experience with machine learning in detail. Be ready to explain how you've framed problems, engineered features, and deployed models in previous roles. This is your chance to showcase your technical ownership and the impact of your work.
✨Speak Their Language
Familiarise yourself with the specific tools and technologies mentioned in the job description, like Python, SQL, and AWS. Prepare examples of how you've used these in a production environment, especially in regulated industries. This will show that you understand their needs and can hit the ground running.
✨Collaboration is Key
Highlight your experience working with cross-functional teams, especially Engineering and Data teams. Be prepared to discuss how you've partnered with others to ensure robust, production-ready solutions. This role requires strong collaboration skills, so demonstrate your ability to work well with different stakeholders.
✨Focus on Governance and Monitoring
Since this role involves working in a regulated environment, be ready to talk about your approach to model governance and monitoring. Discuss any frameworks you've established for performance tracking, drift detection, and retraining. This will show that you think beyond just building models and care about their long-term success.