Credit Strategy Analyst at Updraft

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

17 Feb, 26

Salary

0.0

Posted On

19 Nov, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Credit Risk, Data Analysis, SQL, Python, Risk Analytics, Lending Strategies, Stakeholder Management, Analytical Skills, Credit Scoring Models, Market Trends, Portfolio Analytics, Machine Learning, Decision Engines, Credit Risk Platforms, Customer Profiles, Underwriting Criteria

Industry

Financial Services

Description

Updraft. Helping you make changes that pay off. Updraft is an award winning, FCA-authorised, high-growth fintech based in London. Our vision is to revolutionise the way people spend and think about money, by automating the day to day decisions involved in managing money and mainstream borrowings like credit cards, overdrafts and other loans. A 360 degree spending view across all your financial accounts (using Open banking) A free credit report with tips and guidance to help improve your credit score Native AI led personalised financial planning to help users manage money, pay off their debts and improve their credit scores. Intelligent lending products to help reduce cost of credit We have built scale and are getting well recognised in the UK fintech ecosystem. 800k+ users of the mobile app that has helped users swap c £500 m of costly credit-card debt for smarter credit, putting hundreds of thousands on a path to better financial health The product is highly rated by our customers. We are rated 4.8 on Trustpilot, 4.8 on the Play Store, and 4.4 on the iOS Store. We are selected for Technation Future Fifty 2025 – a program that recognizes and supports successful and innovative scaleups to IPOs - 30% of UK unicorns have come out of this program. Updraft once again featured on the Sifted 100 UK startups - among only 25 companies to have made the list over both years 2024 and 2025. We are seeking a skilled Consumer Credit Risk Strategy Analyst with 3+ years of experience in the UK credit markets, specifically within personal loans for debt consolidation. The successful candidate will play a critical role in assessing, monitoring, and optimising credit risk strategies to ensure a balanced approach between risk management and business growth. Key Responsibilities: Conduct portfolio analytics to assess risk trends, customer behaviour, and loan performance. Analyse credit data, customer profiles, and market trends to enhance underwriting criteria. Work closely with stakeholders to optimise credit policies and improve lending strategies. Utilise statistical and machine learning techniques to support data-driven risk assessment methodologies. Monitor key performance indicators (KPIs) related to loan approvals, delinquencies, and charge-offs. Required Skills & Experience: 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. Relevant experience in working on UK credit bureau data and leveraging it for credit risk analysis. Experience or familiarity with end-to-end life cycle of a retail loan portfolio. 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. Excellent communication and stakeholder management abilities. Preferred Qualifications: 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. Pension Plan Paid Time Off Work From Home Training & Development #LI-A

Responsibilities

The successful candidate will conduct portfolio analytics to assess risk trends and customer behavior while optimizing credit policies and lending strategies. They will utilize statistical and machine learning techniques to support data-driven risk assessment methodologies.