Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Validate data-driven models and enhance validation processes using analytics.
- Company: Join a collaborative community focused on data, machine learning, and statistics.
- Benefits: Enjoy opportunities for career growth, remote work options, and a supportive team culture.
- Why this job: Be part of a mission to improve decision-making through data insights and model risk management.
- Qualifications: Experience in building and validating models, Python programming, and understanding machine learning concepts.
- Other info: Ideal for problem solvers eager to tackle complex challenges in a dynamic environment.
The predicted salary is between 36000 - 60000 £ per year.
Join us as a Model Risk Data Scientist. If you’re a keen problem solver and you’re passionate about data, machine learning and statistics, we think you’ll enjoy a real sense of purpose in this role. You’ll be harnessing your mathematical prowess to validate our data-driven models and explore ways to automate and enhance their validation processes. It’s your chance to be part of a collaborative community of data enthusiasts, discover new algorithms, tools and data ecosystems, and develop specialist knowledge that will see you become an expert in your field – and pave the way for further career success.
What you'll do
In today’s rapidly changing world, our ability to understand tomorrow and make better decisions today is key. And while we can’t see into the future, we can use data-driven models to guide our business practices, identify uncertainties and uncover valuable insights. But reliance on models invariably presents its own risks. As our Model Risk Data Scientist, it’ll be your job to review and independently validate our data-driven models to determine how they’re being used, their accuracy and dependencies, and the level of model risk they pose. Day to day, you’ll be:
- Using data and analytics to review the data-driven models that we use across our bank
- Developing the validation framework for Gen AI models across the bank
- Sharing your findings with your stakeholders and building consensus on how model risks can be mitigated
- Exploring ways to automate and enhance our model validation activities
- Collaborating with model developers to increase the value generated by data-driven modelling
- Developing our analytics codebase by adding new functionality, fixing issues and testing code
The skills you'll need
With practical experience of building and validating data-driven models, you’ll bring the creativity, determination and perseverance that comes with tackling ideas that are hard to understand and problems that are hard to solve. You’ll also bring great working habits too, like being organised, thorough and painstaking in your work, great at working under pressure, and equally content working on your own or together as a team. And you’ll bring this all together with:
- A good understanding of the mathematical methods, concepts and assumptions that underpin machine learning, statistical modelling and artificial intelligence
- Python programming experience and commonly used libraries in data science
- An appreciation of the practicalities that working with real-world datasets presents, and the operational challenge of deploying data-driven models
- Your ability to uncover and extract meaningful insights from technical results and relay these in a way that’s easy to understand
Model Validation Data Scientist employer: NatWest Group
Contact Detail:
NatWest Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Model Validation Data Scientist
✨Tip Number 1
Familiarise yourself with the latest trends in model validation and machine learning. Follow industry leaders on social media, read relevant blogs, and participate in online forums to stay updated. This knowledge will not only help you during interviews but also demonstrate your passion for the field.
✨Tip Number 2
Network with professionals already working in model validation or data science roles. Attend meetups, webinars, or conferences where you can connect with others in the industry. Building these relationships can lead to valuable insights and potential referrals for job openings.
✨Tip Number 3
Showcase your practical experience by working on personal projects or contributing to open-source initiatives related to model validation. This hands-on experience will not only enhance your skills but also provide concrete examples to discuss during interviews.
✨Tip Number 4
Prepare to discuss how you've tackled complex problems in your previous roles. Think of specific examples where you used data-driven models to derive insights or mitigate risks. Being able to articulate your thought process and problem-solving approach will set you apart from other candidates.
We think you need these skills to ace Model Validation Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with data-driven models, machine learning, and statistical analysis. Use specific examples that demonstrate your skills in Python programming and your understanding of mathematical concepts.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data and problem-solving. Mention how your background aligns with the responsibilities of a Model Validation Data Scientist and how you can contribute to the team.
Showcase Relevant Projects: If you've worked on projects involving model validation or data analytics, include these in your application. Describe your role, the challenges faced, and the outcomes achieved to illustrate your practical experience.
Prepare for Technical Questions: Anticipate technical questions related to model validation, machine learning algorithms, and data analysis. Brush up on your knowledge of Python libraries commonly used in data science, as well as the operational challenges of deploying models.
How to prepare for a job interview at NatWest Group
✨Showcase Your Problem-Solving Skills
Be prepared to discuss specific examples where you've tackled complex problems using data. Highlight your analytical thinking and how you approached these challenges, as this role heavily relies on problem-solving abilities.
✨Demonstrate Your Technical Expertise
Make sure to brush up on your knowledge of machine learning, statistical modelling, and Python programming. Be ready to discuss the libraries you’ve used and any projects where you’ve applied these skills, as practical experience is crucial for this position.
✨Understand Model Validation Processes
Familiarise yourself with model validation frameworks and methodologies. Be prepared to explain how you would approach validating data-driven models and mitigating model risks, as this will be a key part of your role.
✨Communicate Clearly and Effectively
Practice explaining complex technical concepts in simple terms. Since you'll need to share findings with stakeholders, being able to convey insights clearly will demonstrate your ability to bridge the gap between technical and non-technical audiences.
