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- Tasks: Lead the development of advanced deep learning solutions for underwriting.
- Company: Join Capital One, a leader in innovative financial technology.
- Benefits: Enjoy a hybrid work model with flexible arrangements and competitive pay.
- Other info: Collaborate with business stakeholders and leverage unique datasets for innovation.
- Why this job: Make a real impact by driving predictive models with cutting-edge technology.
- Qualifications: Strong experience in deep learning frameworks and coding skills in SQL and Python.
The predicted salary is between 70000 - 90000 £ per year.
Capital One in Nottingham is seeking a Lead Data Scientist focused on developing advanced deep learning solutions for underwriting. You will drive the next generation of predictive models, collaborating with business stakeholders while leveraging non-traditional datasets.
The role demands strong experience in deep learning frameworks like PyTorch and TensorFlow, solid coding skills in SQL and Python, and an understanding of probability and statistics. The position offers a hybrid work model with flexible arrangements available.
Lead Deep Learning Scientist for Underwriting ML in Nottingham employer: Capital One
Contact Detail:
Capital One Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Deep Learning Scientist for Underwriting ML in Nottingham
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Capital One. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your deep learning projects, especially those using PyTorch and TensorFlow. This will help us see your expertise in action and set you apart from the crowd.
✨Tip Number 3
Prepare for the interview by brushing up on your SQL and Python coding skills. We want to see how you think and solve problems, so practice coding challenges and be ready to discuss your thought process.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you're genuinely interested in joining our team at Capital One.
We think you need these skills to ace Lead Deep Learning Scientist for Underwriting ML in Nottingham
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with deep learning frameworks like PyTorch and TensorFlow in your application. We want to see your coding skills shine, so don’t forget to mention your proficiency in SQL and Python!
Tailor Your Application: Take a moment to customise your application for the Lead Deep Learning Scientist role. We love seeing how your background aligns with our needs, especially when it comes to developing predictive models and working with non-traditional datasets.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured applications that get straight to the heart of your qualifications and experiences without unnecessary fluff.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for this exciting opportunity at Capital One.
How to prepare for a job interview at Capital One
✨Know Your Deep Learning Frameworks
Make sure you brush up on your knowledge of PyTorch and TensorFlow. Be ready to discuss specific projects where you've used these frameworks, and think about how you can apply them to underwriting solutions.
✨Showcase Your Coding Skills
Prepare to demonstrate your coding abilities in SQL and Python. You might be asked to solve a problem on the spot, so practice coding challenges beforehand to ensure you're comfortable and confident.
✨Understand the Business Context
Familiarise yourself with the underwriting process and how deep learning can enhance predictive models. Being able to connect your technical skills to business outcomes will impress the interviewers.
✨Prepare for Collaboration Questions
Since this role involves working with business stakeholders, think of examples where you've successfully collaborated with non-technical teams. Highlight your communication skills and how you translate complex data insights into actionable strategies.