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For Companies At a Glance
- Tasks: Build credit risk models and scorecards using SQL and Python to drive revenue.
- Company: Join a dynamic lending fintech in London focused on innovation and growth.
- Benefits: Enjoy a hybrid work environment with competitive pay up to £80,000.
- Why this job: Make an impact in the fintech space while developing your data science skills.
- Qualifications: Strong model development experience in lending and proficiency in SQL and Python required.
- Other info: Ideal for those looking to thrive in a fast-paced, collaborative environment.
The predicted salary is between 48000 - 64000 £ per year.
This range is provided by Harnham. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range
Data Scientist (Credit Risk)
London
Hybrid
Up to £80,000
The Company
I am hiring for a lending fintech based in London who is looking to bring in a Credit Risk Data Scientist to build scorecards and other credit risk models across the credit risk customer lifecycle using SQL and Python to drive revenue and profitability.
The Role
What you will do as a Credit Risk Data Scientist:
- Developing scorecards across the credit risk customer lifecycle
- Analyse large datasets to extract meaningful insights to drive model development.
- Developing models working with other functions for collections and acquisitions scorecards
- Developing pricing models for Credit Risk.
- Developing application and behavioural scorecards
- Using SQL and Python daily to develop credit risk models and improve the credit risk scorecard models across the customer lifecycle.
Requirements:
What you need to be successful as a Credit Risk Data Scientist:
- Strong experience developing models within lending.
- Experience at a fintech/fast-paced company
- Strong experience using SQL and Python to develop models.
- Numeric degree from a top university.
Seniority level
- Associate
Employment type
- Full-time
Job function
- Analyst
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Data Scientist (Credit Risk) employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist (Credit Risk)
✨Tip Number 1
Make sure to showcase your experience with SQL and Python in your conversations. These are crucial skills for the role, and demonstrating your proficiency can set you apart from other candidates.
✨Tip Number 2
Familiarize yourself with the latest trends in credit risk modeling. Being able to discuss current methodologies and innovations during your interview will show that you're not only qualified but also passionate about the field.
✨Tip Number 3
Network with professionals in the fintech space. Attend industry events or webinars where you can meet people who work in similar roles. This can provide valuable insights and potentially lead to referrals.
✨Tip Number 4
Prepare to discuss specific projects where you've developed credit risk models. Be ready to explain your thought process, the challenges you faced, and how your contributions drove results.
We think you need these skills to ace Data Scientist (Credit Risk)
Some tips for your application 🫡
Highlight Relevant Experience: Make sure to emphasize your experience in developing credit risk models, particularly within the lending sector. Use specific examples that showcase your skills in SQL and Python.
Showcase Your Technical Skills: Clearly outline your proficiency in SQL and Python. Mention any relevant projects or achievements where you utilized these skills to develop scorecards or analyze large datasets.
Tailor Your CV: Customize your CV to align with the job description. Focus on your analytical skills and any experience in fintech environments, as this is crucial for the role.
Craft a Compelling Cover Letter: Write a cover letter that not only summarizes your qualifications but also expresses your enthusiasm for the role. Discuss how your background makes you a perfect fit for the Credit Risk Data Scientist position.
How to prepare for a job interview at Harnham
✨Showcase Your Technical Skills
Be prepared to discuss your experience with SQL and Python in detail. Bring examples of projects where you've developed credit risk models or scorecards, and be ready to explain your thought process and the impact of your work.
✨Understand the Fintech Landscape
Research the lending fintech industry and be familiar with current trends and challenges. This will help you demonstrate your knowledge and show that you're genuinely interested in the company and its mission.
✨Prepare for Behavioral Questions
Expect questions about how you've worked in fast-paced environments and collaborated with other teams. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your problem-solving skills.
✨Ask Insightful Questions
Prepare thoughtful questions to ask your interviewers about the company's approach to credit risk modeling and their expectations for the role. This shows your enthusiasm and helps you assess if the company is the right fit for you.
