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For Companies At a Glance
- Tasks: Join us as a Risk Data Scientist, delivering robust risk models and frameworks.
- Company: Be part of the UK's largest car leasing company, driving smart and sustainable mobility solutions.
- Benefits: Enjoy a competitive salary, annual bonus, and 15% pension contribution with private medical and dental insurance.
- Why this job: This role offers autonomy, development opportunities, and the chance to impact the automotive industry.
- Qualifications: Strong background in Statistics, Mathematics, or Data Science with experience in Risk Modelling required.
- Other info: Work hybrid in London, collaborating with a team of experts in a dynamic environment.
The predicted salary is between 48000 - 72000 £ per year.
Data Scientist – Risk | Automotive
Risk Data Scientist employer: Peaple Talent
Contact Detail:
Peaple Talent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Risk Data Scientist
✨Tip Number 1
Familiarise yourself with the latest trends in risk modelling, especially within the automotive sector. This will not only help you understand the industry better but also allow you to engage in informed discussions during interviews.
✨Tip Number 2
Network with professionals in the automotive and data science fields. Attend relevant meetups or webinars to connect with potential colleagues or mentors who can provide insights into the company culture and expectations.
✨Tip Number 3
Brush up on your Python or R skills, focusing on statistical software and machine learning techniques. Being able to demonstrate your technical proficiency in these areas will set you apart from other candidates.
✨Tip Number 4
Prepare specific examples of your past work in risk modelling and validation. Be ready to discuss how you've implemented robust models and the impact they had on your previous organisations, as this will showcase your practical experience.
We think you need these skills to ace Risk Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in Risk Modelling and Data Science. Use specific examples that demonstrate your skills in statistics, machine learning, and predictive modelling, particularly in the automotive or finance sectors.
Craft a Compelling Cover Letter: Write a cover letter that connects your background in Statistics, Mathematics, or Economics to the role. Emphasise your experience with statistical software like Python or R, and how it aligns with the company's needs for robust model risk management.
Showcase Relevant Projects: If you have worked on projects related to risk management or forecasting, include them in your application. Detail your role, the techniques used, and the outcomes achieved to demonstrate your practical experience.
Proofread Your Application: Before submitting, carefully proofread your application for any errors or inconsistencies. A well-presented application reflects your attention to detail, which is crucial in data science roles.
How to prepare for a job interview at Peaple Talent
✨Showcase Your Statistical Skills
Make sure to highlight your strong background in statistics and mathematics during the interview. Be prepared to discuss specific statistical methods you've used in risk modelling and how they contributed to successful outcomes in your previous roles.
✨Demonstrate Your Technical Proficiency
Since experience with statistical software like Python or R is crucial, be ready to provide examples of projects where you utilised these tools. Discuss any advanced analytical techniques you've applied, such as machine learning or predictive modelling, to showcase your technical expertise.
✨Understand the Industry Context
Familiarise yourself with the automotive and finance sectors, particularly in relation to risk management. Being able to discuss industry trends and challenges will demonstrate your knowledge and interest in the field, making you a more attractive candidate.
✨Prepare for Scenario-Based Questions
Expect scenario-based questions that assess your problem-solving skills in risk modelling. Prepare to explain how you would approach specific risk scenarios, including the methodologies you would use and the rationale behind your decisions.
