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- 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:
- 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.
- 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 specific data-driven models used in the banking sector. Understanding their applications and limitations will help you engage in meaningful discussions during interviews and demonstrate your expertise.
✨Tip Number 2
Network with professionals in the field of model validation and data science. Attend relevant meetups or webinars to connect with others who work in similar roles, as they can provide insights and potentially refer you to opportunities at StudySmarter.
✨Tip Number 3
Brush up on your Python programming skills, especially focusing on libraries commonly used in data science like Pandas, NumPy, and Scikit-learn. Being proficient in these tools will not only boost your confidence but also make you a more attractive candidate.
✨Tip Number 4
Prepare to discuss real-world datasets and the challenges they present. Think of examples where you've successfully navigated these issues, as this will showcase your practical experience and problem-solving abilities during the interview process.
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 methods.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data and problem-solving. Mention how your background aligns with the role of Model Validation Data Scientist and how you can contribute to the team’s goals.
Showcase Relevant Projects: If you have worked on projects involving model validation or data analytics, include them in your application. Describe your role, the challenges faced, and the outcomes achieved to illustrate your hands-on experience.
Prepare for Technical Questions: Anticipate technical questions related to model validation, machine learning algorithms, and data analysis. Be ready to discuss your thought process and problem-solving approach during interviews.
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 the 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 technical proficiency is crucial for this position.
✨Understand Model Risk and Validation
Familiarise yourself with model risk concepts and validation frameworks. Be prepared to explain how you would assess model accuracy and dependencies, and discuss any experience you have in mitigating model risks.
✨Communicate Clearly with Stakeholders
Since sharing findings and building consensus is part of the role, practice explaining complex technical concepts in simple terms. Think of examples where you've successfully communicated insights to non-technical stakeholders.
