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- Tasks: Validate data-driven models and enhance validation processes using data and analytics.
- Company: Join a collaborative community focused on data, machine learning, and statistics.
- Benefits: Gain specialist knowledge, career growth opportunities, and work with cutting-edge technology.
- Why this job: Be part of a team that drives better 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: Opportunity to explore automation in model validation and collaborate with diverse teams.
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, 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 risk management. This will not only help you understand the current landscape but also allow you to engage in meaningful conversations during interviews, showcasing your passion and knowledge in the field.
✨Tip Number 2
Network with professionals in the data science community, especially those who specialise in model validation. Attend relevant meetups or webinars to gain insights and potentially find a mentor who can guide you through the application process.
✨Tip Number 3
Brush up on your Python programming skills, particularly focusing on libraries commonly used in data science like Pandas, NumPy, and Scikit-learn. Being able to demonstrate your coding proficiency can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss real-world examples of how you've tackled complex data problems in the past. Be ready to explain your thought process and the impact of your solutions, as this will highlight your problem-solving abilities and practical experience.
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 relevant experience in data science, model validation, and programming skills, particularly in Python. Use specific examples to demonstrate your problem-solving abilities and familiarity with machine learning concepts.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data and model validation. Discuss how your skills align with the job description and mention any specific projects or experiences that showcase your expertise in the field.
Showcase Your Technical Skills: Include a section in your application that outlines your technical skills, especially those related to data analysis, statistical modelling, and machine learning. Mention any relevant tools or libraries you are proficient in, such as NumPy, pandas, or scikit-learn.
Prepare for Potential Assessments: Be ready for possible technical assessments or coding challenges as part of the application process. Brush up on your Python skills and be prepared to discuss your approach to validating models and handling real-world datasets.
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 requires a keen problem solver.
✨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 that showcase your ability to validate models effectively.
✨Communicate Clearly with Stakeholders
Since sharing findings and building consensus is key in this role, practice explaining technical concepts in simple terms. Think about how you can convey complex ideas clearly to non-technical stakeholders.
✨Prepare for Scenario-Based Questions
Expect questions that ask how you would handle specific model validation scenarios. Think through potential risks and how you would mitigate them, as well as ways to enhance validation processes.
