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- Tasks: Transform complex datasets into actionable insights and solve real-world problems.
- Company: Join a leading financial institution driving digital transformation.
- Benefits: Flexible locations, hybrid working, and long-term career development.
- Other info: Opportunity to work on high-visibility projects within a prestigious organisation.
- Why this job: Shape business strategy with cutting-edge analytics in a collaborative environment.
- Qualifications: Strong statistical analysis, Python programming, and experience with large datasets.
The predicted salary is between 60000 - 80000 £ per year.
We are currently partnering with a leading financial institution to recruit an experienced Senior Data Scientist to join a high-impact analytics and innovation team. This is a fantastic opportunity for a data professional who is passionate about using advanced analytics, AI, and machine learning to influence strategic decision-making at scale.
As a Senior Data Scientist, you will play a key role in transforming complex and high-volume datasets into meaningful, actionable insights that directly support business growth, innovation, and competitive advantage. Working closely with a range of stakeholders across the bank, you’ll apply advanced analytical techniques to solve real-world problems within a highly regulated environment.
This role offers exposure to cutting-edge data science initiatives, alongside the chance to influence long-term strategy within one of the UK’s most established financial services organisations.
Key Responsibilities:- Analyse large, complex datasets to uncover trends, patterns, and insights that inform strategic decisions
- Design, develop, and deploy statistical and machine learning models to solve business problems
- Apply AI and advanced analytics techniques to enhance decision-making and operational efficiency
- Write high-quality, production-ready Python code and contribute to shared analytics codebases
- Query and manipulate large datasets using SQL and PySpark in distributed data environments
- Communicate analytical insights clearly to both technical and non-technical stakeholders
- Ensure strong governance, risk, and control standards are embedded in all analytical work
- Strong background in statistical analysis and modelling
- Advanced programming skills in Python
- Experience working with large datasets using SQL and/or PySpark
- Advanced knowledge of machine learning and AI techniques
- Experience within financial services or strong understanding of banking products, risk frameworks, and regulation
- Data visualisation skills, with the ability to present complex insights clearly and effectively
You may also be assessed on broader competencies such as risk and controls, business acumen, strategic thinking, change and transformation, and digital and technology capability.
Why Apply? Join a globally recognised financial institution undergoing significant digital and data transformation. Work on high-visibility projects that directly shape business strategy. Access long-term career development and progression opportunities. Flexible location options across London, Glasgow, or Northampton. Hybrid working model and supportive, collaborative team culture.
If you’re an experienced Data Scientist looking to take the next step in your career within a prestigious and forward-thinking organisation, we’d love to hear from you. Apply in the first instance with your CV.
Senior Data Scientist in London employer: Marshall Wolfe
Contact Detail:
Marshall Wolfe Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist in London
✨Tip Number 1
Network like a pro! Reach out to connections in the financial sector, especially those who work at the institution you're eyeing. A friendly chat can open doors and give you insider info that could set you apart.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your best data science projects, especially those involving machine learning and AI. This will help you demonstrate your expertise and passion during interviews.
✨Tip Number 3
Practice makes perfect! Brush up on your Python and SQL skills, and be ready to tackle technical questions or coding challenges. We all know that confidence in your abilities can make a huge difference.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you’re genuinely interested in joining our team and contributing to exciting data initiatives.
We think you need these skills to ace Senior Data Scientist in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Senior Data Scientist role. Highlight your background in statistical analysis, Python programming, and any experience with large datasets. We want to see how you can bring value to our team!
Showcase Your Projects: Include specific examples of projects where you've applied advanced analytics or machine learning techniques. This helps us understand your hands-on experience and how you tackle real-world problems. Don't be shy about sharing your successes!
Keep It Clear and Concise: When writing your application, clarity is key! Use straightforward language and avoid jargon where possible. We appreciate a well-structured application that communicates your insights effectively, just like you would with stakeholders.
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of being noticed. It’s the easiest way for us to keep track of your application and ensure it gets to the right people. We can't wait to hear from you!
How to prepare for a job interview at Marshall Wolfe
✨Know Your Data Inside Out
Before the interview, dive deep into the types of datasets you might be working with. Familiarise yourself with common trends and patterns in financial data, as well as the specific analytical techniques that can be applied. This will not only help you answer technical questions but also demonstrate your passion for the role.
✨Showcase Your Coding Skills
Be prepared to discuss your experience with Python, SQL, and PySpark. Bring examples of your past projects where you've written production-ready code or developed machine learning models. If possible, have a portfolio ready to showcase your work, as this can set you apart from other candidates.
✨Communicate Clearly and Effectively
Since you'll be working with both technical and non-technical stakeholders, practice explaining complex analytical concepts in simple terms. Think of examples where you've successfully communicated insights in the past, and be ready to share these during the interview.
✨Understand the Business Context
Research the financial institution and its products thoroughly. Understand the regulatory environment and how it impacts data science initiatives. This knowledge will help you align your answers with the company's strategic goals and show that you're not just a data expert, but also a business-savvy candidate.