At a Glance
- Tasks: Lead credit risk analytics and develop statistical models for innovative lending strategies.
- Company: Join a dynamic banking sector team focused on credit risk analytics.
- Benefits: Competitive salary, professional development, flexible working, and a collaborative environment.
- Why this job: Make an impact in credit risk while mentoring junior analysts and enhancing your skills.
- Qualifications: 6-10 years in credit risk analytics with strong analytical and programming skills.
- Other info: Opportunity for career progression in a diverse and inclusive workplace.
The predicted salary is between 36000 - 60000 ÂŁ per year.
We are seeking a talented Principal Credit Risk Analyst to join our growing Credit Risk Analytics team. This role is specifically designed for professionals with 6-10 years of experience in the lending industry who are looking to develop their career in credit risk analytics and statistical modelling within the banking sector.
About the Role
As a Principal Credit Risk Analyst, you will contribute to the development, daily management and enhancement of our credit risk analytics capabilities. This is an excellent opportunity for someone with experience in unsecured lending to get wider exposure and larger remit whilst working with cutting‑edge statistical models, data‑driven insights and best‑in‑class datasets. The role focuses on the UK market and requires experience of the UK lending landscape, but you will have the opportunity to work in a pan‑European team and get exposure to credit risk analytics across our international markets including Germany, Austria, Norway, Spain, Italy, and other European markets. The role is ideal for a very experienced analyst who wants to stay hands‑on while developing their mentoring/supervising and DS/analytical product management skills.
Key Responsibilities
- Data Analysis & Insights: Opportunity to own end‑to‑end strategies for specific population segments. Collect, analyse, and interpret data from multiple sources including internal systems, open banking and Credit Reference Agencies (CRAs) to identify trends, patterns, and opportunities that inform credit policy, credit limit and pricing strategies.
- Model Development: Developing and maintaining statistical models, including scorecards, ML models and other credit risk assessment tools.
- Stakeholder Collaboration: Provide clear, actionable insights and recommendations to stakeholders across the business, supporting informed decision‑making and business growth.
- Cross‑functional Teamwork: Work collaboratively with colleagues in the decision sciences and analytics team, as well as other departments across different locations.
- Communication & Reporting: Present complex analytical findings to both technical and non‑technical audiences through clear presentations, reports, and data visualisations.
- Mentoring: Mentor and supervise junior analysts on model and strategy development projects, and Python model pipeline development.
Essential Requirements
- Education & Experience: Bachelor degree in Mathematics, Statistics, Economics, Physics, Computer Science, Engineering, or related quantitative discipline from a well‑regarded university. 6-10 years of experience in analytics, data analysis, or lending/credit assessment within the financial services sector. Previous experience working for an unsecured lender, preferably on Credit card products.
- Technical Skills: Strong analytical and problem‑solving abilities with excellent attention to detail. Proficiency in SQL for data extraction, manipulation, and analysis. Strong programming experience in Python (ideally for model development). Understanding of statistical analysis techniques and data modelling methodologies. Experience in predictive modelling (logistic regression and GBM knowledge at minima). Understanding of machine learning techniques applied to credit risk.
- Professional Skills: Excellent written and verbal communication skills in English. Ability to translate complex data into clear, actionable business insights. Strong presentation skills with experience communicating to diverse audiences. Proven ability to work effectively in fast‑paced environments whilst managing multiple priorities. Team player.
Desirable Requirements
- Postgraduate qualification in a relevant quantitative field.
- Knowledge of credit risk regulations and best practices in the UK market.
- Experience with data visualisation tools.
- Experience of full credit card customer journey (from origination to recoveries).
- Experience with model pipeline maintenance.
- Experience with model monitoring.
- Experience in analytics/DS project management.
What We Offer
- Competitive salary and benefits package.
- Opportunity to work with advanced analytics and statistical modelling techniques.
- Professional development and training opportunities.
- Collaborative, inclusive working environment.
- Flexible working arrangements.
- Career progression opportunities within our growing analytics function.
Please note: We are unable to provide visa sponsorship for this role. Candidates must have the right to work in the UK without requiring sponsorship. We are committed to creating an inclusive workplace that reflects the diversity of the communities we serve. We welcome applications from all qualified candidates regardless of age, disability, gender identity, race, religion, sexual orientation, or background.
Principal Credit Risk Analyst in City of Westminster employer: TF Bank AB
Contact Detail:
TF Bank AB Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Principal Credit Risk Analyst in City of Westminster
✨Tip Number 1
Network like a pro! Reach out to your connections in the lending industry and let them know you're on the hunt for a Principal Credit Risk Analyst role. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Get your LinkedIn game on point! Make sure your profile showcases your experience in credit risk analytics and statistical modelling. Join relevant groups and engage with posts to increase your visibility in the industry.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills. Be ready to discuss your experience with SQL, Python, and predictive modelling techniques. Practice explaining complex data insights in simple terms – it’ll impress those non-technical interviewers!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Principal Credit Risk Analyst in City of Westminster
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in credit risk analytics and statistical modelling. We want to see how your background aligns with the role, so don’t be shy about showcasing your relevant skills and achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about credit risk and how your experience makes you the perfect fit for our team. Keep it engaging and personal – we love to see your personality come through.
Showcase Your Technical Skills: Since this role requires strong analytical abilities, make sure to highlight your proficiency in SQL and Python. If you’ve worked on predictive modelling or machine learning techniques, give us the details – we want to know what you can bring to the table!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining our awesome team at StudySmarter!
How to prepare for a job interview at TF Bank AB
✨Know Your Numbers
As a Principal Credit Risk Analyst, you'll need to demonstrate your analytical prowess. Brush up on key metrics and statistical models relevant to credit risk assessment. Be ready to discuss how you've used data to drive decisions in previous roles.
✨Showcase Your Technical Skills
Make sure you highlight your proficiency in SQL and Python during the interview. Prepare examples of how you've developed or maintained statistical models, and be ready to explain your approach to predictive modelling and machine learning techniques.
✨Communicate Clearly
You’ll need to present complex findings to both technical and non-technical audiences. Practice explaining your past projects in simple terms, focusing on the insights gained and their impact on business strategies. Clear communication is key!
✨Be a Team Player
Collaboration is crucial in this role. Share examples of how you've worked with cross-functional teams in the past. Highlight your mentoring experience and how you've supported junior analysts, as this will show your leadership potential.