TL;DR
Consumer Credit Risk Strategy Analyst (Fintech): Assessing, monitoring, and optimising credit risk strategies for personal loans with an accent on portfolio analytics, underwriting criteria, and lending strategies. Focus on utilising statistical and machine learning techniques for data-driven risk assessment and improving financial health through smarter credit.
Location: Remote
Company
hirify.global is an award-winning, FCA-authorised, high-growth fintech based in London, revolutionising how people manage money and mainstream borrowings.
What you will do
- Conduct portfolio analytics to assess risk trends, customer behaviour, and loan performance.
- Analyse credit data, customer profiles, and market trends to enhance underwriting criteria.
- Optimise credit policies and improve lending strategies in collaboration with stakeholders.
- Utilise statistical and machine learning techniques for data-driven risk assessment methodologies.
- Monitor key performance indicators (KPIs) related to loan approvals, delinquencies, and charge-offs.
Requirements
- 3+ years of experience in a credit risk role, preferably within personal loans and debt consolidation in the UK.
- Strong understanding of credit scoring models, risk analytics, and lending strategies.
- Experience in working on UK credit bureau data and leveraging it for credit risk analysis.
- Proficiency in SQL and Python for data analysis and risk modeling.
- Experience working with decision engines and credit risk platforms.
- Strong analytical skills with the ability to translate data insights into business decisions.
Nice to have
- Experience in alternative data usage for credit risk evaluation.
- Familiarity with Open Banking and fintech lending platforms.
- Bachelor’s or Master’s degree in Finance, Statistics, Mathematics, Economics, or a related field.
Culture & Benefits
- Pension Plan
- Paid Time Off
- Work From Home
- Training & Development
Будьте осторожны: если вас просят войти в iCloud/Google, прислать код/пароль, запустить код/ПО, не делайте этого - это мошенники. Обязательно жмите "Пожаловаться" или пишите в поддержку. Подробнее в гайде →