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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: Gain specialist knowledge, career growth opportunities, and work with innovative tools.
- Why this job: Be part of a team that drives impactful decisions through data insights.
- Qualifications: Experience in building and validating models, Python programming, and understanding machine learning concepts.
- Other info: Ideal for problem solvers who thrive under pressure and enjoy teamwork.
The predicted salary is between 28800 - 48000 £ per year.
Join us as a Model Risk Data Scientist
- If you have experience building and validating AI and machine learning models, this is a fantastic opportunity join our innovative, vibrant team in our Risk function
- You’ll be performing technical reviews and oversight of AI models used in the bank, whilst working with model development teams
What you’ll do
This Model Risk Data Scientist role will see you reviewing and independently validating assigned models in accordance with the bank’s policies and model standards. You’ll be responsible for designing and developing an evaluation framework for Gen AI and agentic AI models.
As well as this, you’ll be developing solutions for automating validation activities while understanding model and data usage, quality and interdependencies across the bank.
Your role will also involve:
- Developing the team’s analytics codebase, adding functionality, fixing issues and testing code
- Conducting research on latest LLM evaluation methods based on use case specific challenges
- Contributing to the development of an efficient and scalable evaluation package to be used by the independent validation function
- Reviewing your colleagues’ analysis, code and reports
- Representing the team at model governance forums and other meetings
- Assisting the leadership team with managing the team’s tasks and workflow and helping your team with their training and development
The skills you’ll need
We’re looking for someone with an excellent grasp of mathematical methods, concepts and assumptions that underpin machine learning, statistical modelling and artificial intelligence.
You’ll also need:
- A proficiency in Python and libraries commonly used for data science, such as Linux WSL and AWS Sagemaker
- Practical experience building and validating Large Language Models
- The ability to extract the essential ideas underlying technical results and explain them in terms of their practical consequences
- The ability to deal with ambiguity and to work autonomously
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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 in interviews but also demonstrate your passion for the field.
✨Tip Number 2
Network with professionals in the data science community. Attend meetups, webinars, or conferences related to model validation and data science. Building connections can lead to valuable insights and potential referrals for job openings at StudySmarter.
✨Tip Number 3
Showcase your practical experience by working on personal projects or contributing to open-source initiatives. Create a portfolio that highlights your skills in Python programming and model validation. This hands-on experience will make you stand out during the selection process.
✨Tip Number 4
Prepare for technical interviews by practising problem-solving scenarios related to model validation. Use platforms like LeetCode or HackerRank to sharpen your coding skills. Being well-prepared will boost your confidence and improve your chances of impressing our hiring team.
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 responsibilities of the Model Validation Data Scientist role 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 hands-on experience.
Prepare for Technical Questions: Anticipate technical questions related to model validation, machine learning algorithms, and data analysis. Brush up on key concepts and be ready to discuss how you've applied them in real-world scenarios.
How to prepare for a job interview at NatWest Group
✨Showcase Your Problem-Solving Skills
As a Model Validation Data Scientist, you'll need to demonstrate your ability to tackle complex problems. Prepare examples from your past experiences where you successfully solved challenging issues using data-driven approaches.
✨Highlight Your Technical Expertise
Make sure to discuss your experience with Python and relevant libraries in data science. Be ready to explain how you've used these tools in previous projects, especially in building and validating models.
✨Understand the Business Context
Familiarise yourself with the company's operations and how data-driven models impact decision-making. This will help you articulate how your role as a Model Validation Data Scientist can add value to their business practices.
✨Prepare for Collaborative Scenarios
Since collaboration is key in this role, think of instances where you've worked effectively with others, particularly model developers. Be prepared to discuss how you can build consensus on model risks and enhance validation processes together.
