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- Tasks: Drive Noggin's data strategy and develop credit default models.
- Company: Join a North East startup with an all-female founding team focused on inclusivity.
- Benefits: Enjoy share options, 25 days holiday, and personal development days.
- Why this job: Be part of a mission to revolutionize credit access and shape the future of fintech.
- Qualifications: 5+ years in credit risk, experience with scorecards, and proficiency in Python required.
- Other info: Hybrid working available; candidates from the North East encouraged to apply.
The predicted salary is between 36000 - 60000 £ per year.
About Us: We are a North East startup backed by Bethnal Green Ventures and Oxford Capital, with an all-female founding team dedicated to inclusivity. Our mission is to revolutionise access to credit, ensuring equitable access for all. If you are committed and ready to contribute to reshaping the credit landscape, we’d love to hear from you! Your role: As one of Noggin’s early employees, you will be responsible for several tasks to drive the business forward, including: Owning the development of Noggin’s alternative data credit default models, continuing more exploratory approaches and building scorecards. Shaping the direction of Noggin’s data strategy alongside the founders. Developing and owning a continuous improvement programme of work. Robustly testing Noggin’s credit default models and developing a defence of the approach that will hold up to scrutiny with regulators. Manage stakeholder relationships with key advisors, including involvement in future fundraising rounds. Requirements: to excel in this role, you will: Be a talented credit risk professional, who has 5+ years of experience working at a consumer Lender or Credit Reference Agency (ideal), or be ready to bring a strong fintech background to the role. Have previous experience building consumer credit risk scorecards. Have previous experience working with Open Banking data and categorisations. Be comfortable with Python and the Python data science ecosystem: Pandas, NumPy, matplotlib, scikit-learn, and have experience extracting features from time-series datasets. Experienced in the full Machine Learning delivery lifecycle. This should include designing, training, validating, deploying and monitoring Machine Learning models. Be commercially minded and focused on shaping / delivering against Noggin’s business objectives. How we work: We have an office based in Newcastle upon Tyne where the founding team works daily. We offer hybrid working and are happy to adapt this job to best suit your needs, though we do encourage candidates to apply who live in the North East. Benefits: – Share options. – 25 days holiday + bank holidays. – x1 personal development day per month. – Noggin’s Climbing Club
Data Scientist (Credit Risk) employer: Noggin HQ
Contact Detail:
Noggin HQ Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist (Credit Risk)
✨Tip Number 1
Familiarize yourself with the specific credit risk models and methodologies used in the fintech industry. Understanding the nuances of alternative data credit default models will give you an edge during discussions with the team.
✨Tip Number 2
Showcase your experience with Open Banking data in your conversations. Being able to discuss how you've utilized this data in previous roles can demonstrate your readiness to contribute to Noggin's mission.
✨Tip Number 3
Prepare to discuss your approach to stakeholder management, especially in relation to fundraising. Highlighting your experience in building relationships with key advisors can set you apart as a candidate who understands the business landscape.
✨Tip Number 4
Be ready to talk about your experience with the full Machine Learning delivery lifecycle. Sharing specific examples of how you've designed, trained, and deployed models will show that you have the practical skills needed for this role.
We think you need these skills to ace Data Scientist (Credit Risk)
Some tips for your application 🫡
Understand the Company Mission: Before applying, take some time to understand Noggin's mission to revolutionise access to credit. Reflect this understanding in your application to show that you align with their values.
Highlight Relevant Experience: Make sure to emphasize your 5+ years of experience in credit risk, particularly if you've worked at a consumer lender or credit reference agency. Include specific examples of building credit risk scorecards and working with Open Banking data.
Showcase Technical Skills: Clearly outline your proficiency in Python and the data science ecosystem. Mention your experience with libraries like Pandas, NumPy, and scikit-learn, as well as your familiarity with the full Machine Learning delivery lifecycle.
Tailor Your Application: Customize your CV and cover letter to reflect how your skills and experiences directly relate to the responsibilities outlined in the job description. This will help you stand out as a candidate who is genuinely interested in the role.
How to prepare for a job interview at Noggin HQ
✨Show Your Passion for Inclusivity
Since the company is dedicated to inclusivity, make sure to express your commitment to this value during the interview. Share any relevant experiences or projects that demonstrate your dedication to equitable access in the credit landscape.
✨Highlight Your Technical Skills
Be prepared to discuss your experience with Python and the data science ecosystem. Provide specific examples of how you've used tools like Pandas, NumPy, and scikit-learn in previous roles, especially in building credit risk scorecards.
✨Discuss Your Experience with Credit Risk Models
Since the role involves developing alternative data credit default models, be ready to talk about your past experiences in this area. Explain your approach to model development, testing, and how you ensure compliance with regulatory standards.
✨Demonstrate Commercial Awareness
The company is looking for someone who is commercially minded. Prepare to discuss how your work in data science can align with and support Noggin's business objectives, and share any insights you have on the fintech landscape.
