TF Avarda Bank is a digital bank offering consumer banking services and e-commerce solutions through a proprietary IT platform with a high degree of automation. The platform is designed for scalability and adaptability to different products, countries, currencies and digital banking solutions. TF Avarda Bank prioritizes organic growth under controlled conditions and expansion is taking place in carefully selected segments and markets. Operations are conducted in the Nordics, the Baltics, Poland, Germany, Austria, Spain, Ireland, the Netherlands and Italy through subsidiary, branch, or cross-border banking with the support of the Swedish banking license.
The business is divided into three segments: Credit Cards, Ecommerce Solutions and Consumer Lending. The target group for all services is creditworthy private individuals, and the loan amounts are relatively small with short repayment terms. TF Bank also offers deposit products in several markets.
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.
Please note: This position is for a Principal Credit Risk Analyst role focused on credit risk assessment and modelling, not financial analysis.
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 (affordability strategy (including use of Open Banking and other alternative data sources), underwriting scorecards and credit strategy, pricing models and strategies, low and grow strategies, pre-collection strategies, launch of new credit products) 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 some 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 (bank, building society, fintech, or similar credit provider)
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
Application Requirements
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.
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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.