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For Companies At a Glance
- Tasks: Lead credit risk analytics, optimise pricing, and drive data-driven decisions.
- Company: Join a dynamic fintech company focused on innovation and growth.
- Benefits: Competitive salary, flexible working, and opportunities for professional development.
- Why this job: Make a real impact on credit strategies and product performance with cutting-edge technology.
- Qualifications: Experience in decision science and credit risk analytics, ideally in financial services.
- Other info: Fast-paced environment with excellent career growth potential.
The predicted salary is between 36000 - 60000 £ per year.
As a Credit Risk Analytics Lead you will lead on decision science, credit models, portfolio performance and pricing, and play a key role in driving fundamental improvements in our credit and product performance, policy and strategy. You will be a senior member of the team responsible for producing tangible insights to the business which will not only monitor and control risk, but also help scale and grow our products through data driven decisions. This makes you the go-to person when the business requires insights on customer behaviour and any key performance metrics which include profitability, credit risk performance and operational performance metrics. This role also has responsibility for setting and optimising product pricing, that is market competitive while providing the coverage and margins required to deliver value to Kriya.
What You'll Do
- Work closely with our risk leadership team to provide insights to optimise our underwriting and credit decisioning processes.
- Leverage latest data science, machine learning and artificial intelligence to build systematic risk models for our credit products.
- Produce portfolio monitoring and investigative analysis to explain trends and answer questions from key stakeholders; making recommendations on how to enhance our products and processes.
- Develop and optimise our pricing models using financial modelling techniques.
- Produce portfolio management information for both internal and external stakeholders and present insight and findings in a clear non-technical way that is easy for stakeholders to digest and understand.
- Conduct rigorous impact analysis on credit strategies, reassessing and optimising how we make credit decisions.
- Support the collections and recovery team with data driven insight and collections strategies.
- Collaborate with other teams in developing our predictive models, product features and processes.
Qualifications
- Proven experience in decision science, credit risk analytics, data modelling and AI, ideally within financial services or fintech.
- Strong understanding of risk disciplines including unit profitability evaluation, pricing, risk appetite setting, and credit policy design.
- Experience with B2B/SME lending is highly desirable, though not essential.
- Demonstrated ability to influence and collaborate with cross-functional teams and senior stakeholders.
- Strong leadership and people management skills with a track record of building high-performing teams.
- Exceptional analytical and problem-solving abilities with a hands-on approach.
- Excellent communication skills, particularly in translating complex findings into accessible, actionable insights.
- Organised, proactive, and able to manage multiple priorities across different products and development stages.
- Comfortable working in a fast-paced, evolving environment with a start-up / build-it-yourself mindset.
Credit Risk Analytics Lead employer: LGBT Great
Contact Detail:
LGBT Great Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Credit Risk Analytics Lead
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend events, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Prepare a portfolio or case studies that highlight your experience in credit risk analytics and decision science. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Ace the interview! Research common questions for credit risk roles and practice your responses. Be ready to discuss how you've used data-driven insights to influence decisions and improve performance.
✨Tip Number 4
Apply through our website! We love seeing candidates who are genuinely interested in joining us. Tailor your application to showcase how your skills align with our mission and values.
We think you need these skills to ace Credit Risk Analytics Lead
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Credit Risk Analytics Lead. Highlight your experience in decision science and credit risk analytics, and don’t forget to showcase any relevant projects or achievements that align with our needs.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to tell us why you’re passionate about credit risk analytics and how your skills can help drive improvements in our products and processes. Keep it engaging and personal!
Showcase Your Analytical Skills: We love data-driven insights! In your application, provide examples of how you've used data science or machine learning in past roles. This will demonstrate your ability to produce tangible insights that can influence business decisions.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at LGBT Great
✨Know Your Numbers
As a Credit Risk Analytics Lead, you'll need to demonstrate your understanding of key metrics. Brush up on portfolio performance indicators, credit risk metrics, and pricing strategies. Be ready to discuss how you've used data to drive decisions in past roles.
✨Showcase Your Technical Skills
Familiarise yourself with the latest in data science, machine learning, and AI. Prepare examples of how you've applied these technologies in credit risk analytics. This will show that you can leverage cutting-edge tools to enhance product performance.
✨Communicate Clearly
You’ll be translating complex data into actionable insights for stakeholders. Practice explaining your past projects in simple terms. This will help you convey your findings effectively during the interview, showcasing your communication skills.
✨Demonstrate Leadership
As a senior member of the team, your leadership skills are crucial. Prepare to discuss your experience in managing teams and influencing cross-functional collaboration. Highlight specific instances where your leadership made a tangible impact on project outcomes.
