Head of Credit Risk Modelling & Decision Science

Head of Credit Risk Modelling & Decision Science

3 days ago Be among the first 25 applicants

One-third of the UK working-age population is not able to access mainstream financial services. These people find themselves excluded from affordable credit and treated poorly by mainstream financial institutions. Too few are successfully supported on the journey to financial health. Our purpose is “To improve the nation’s financial health through accessibility, affordability and community.”

We are a fast-growing social FinTech company giving not-for-profit Credit Unions in the UK access to a state-of-the-art fintech. We aim to grow a select group of Community Lenders into a network of challenger banks offering a viable alternative to high-cost lenders.

We are a small and dynamic team of 250+ people, offering you the opportunity to have an immediate impact on the business and grow with us. We have over 120,000+ customers on our platform and it’s increasing rapidly.

We grew significantly in size over the last year and the credit unions on our platform are the biggest players in the UK.

The Role:

At Amplifi, data lies at the heart of all strategies. We strongly believe that innovative use of data and AI is the key to delivering on our strategic growth objectives. We are always looking to push the boundaries of what can be achieved through intelligent use of data, and are constantly looking to incorporate new and disparate, sometimes unconventional, data sources and modern data, analytics and modelling technologies into our decision-making. The Head of Data Science role lies at the centre of achieving this objective.

As the Head of Data Science, you are expected to build and lead a team of decision scientists to deliver statistical models that solve real-life business problems and drive strategic business objectives. This role reports directly into the Managing Director and is responsible for building out the team whilst also remaining hands-on with some of the model development initially.

Responsibilities:

• Work with the business strategy teams to identify decision science problems that offer the greatest opportunities to the organisation
• Lead the development of key credit risk models, ensuring they provide the business with a strategic edge for growth and risk management
• Summarise and present recommendations and proposals to C-level execs and external stakeholders (such as partners and investors) with actionable insights.
• Explore large sets of structured and unstructured data from disparate sources, including new, and unconventional ones, and come up with innovative ways of using this data. Design appropriate tests to collect additional data, if required
• Provide thought leadership on advances in Data Science, identifying opportunities within the business for the execution of new ideas, tools and platforms.
• Combine traditional modelling techniques with cutting edge algorithms to build sophisticated modelling solutions to predict various aspects of customer behaviour, competitive landscape, market movements, which help shape through-the-lifecycle strategies relating to Credit Risk Underwriting, Fraud prevention, Pricing, Customer Retention and Value Management, Collections and Customer Services
• Work with wider Data Engineering, Decision Systems and ML Ops teams to ensure proper testing, validation and deployment of ML models in live environments and their ongoing performance monitoring.
• Create and maintain guidelines for model development, validation and testing as well as documentation to ensure consistency, efficiency and best practices.
• Working with Data Engineering, and ML Ops teams, manage the development and maintenance of high-quality data structures and feature stores to facilitate efficient and scalable model building and reporting.
• Hire, manage and mentor team of decision scientists.

Requirements:

This is a high impact role in a fast-growing business and hence the ideal candidate would be someone who:

• Is passionate about Data Science, Modelling and Analytics
• Is self-motivated and proactive; shows ownership and initiative – Not afraid of being hands-on and possess a roll-up-your-sleeves attitude to get things done
• Has excellent communication and stakeholder management skills

To be successful in the role, the candidate should:

• Ideally have 5+ Years of experience in Modelling / Data Science disciplines
• Be experienced in modelling project management, from initial conception and approval through to final delivery, across multidisciplinary teams
• Have proven experience and ability to train others in coding and modelling, using Python / SQL, with high coding standards
• Hold in-depth practical understanding of the content, format and subtleties of UK bureau data (e.g. Experian, Equifax, TransUnion)
• Be an expert in probability and statistics
• Possess proven expertise in traditional credit risk modelling techniques
• Have a strong understanding and genuine interest in machine learning (ML), deep learning, decision trees, random forests, GBM, SVM, naïve Bayes, anomaly detection, clustering
• Understand basics of data pipelines and ML Ops
• Preferably have a degree in a numerate (STEM) discipline or else have equivalent skills derived from self-learning / online courses combined with real-life modelling experience. (Feel free to share link to existing git projects)

Desirable Requirements:

Financial services experience, particularly consumer credit

Scale-up experience

Benefits:

• Competitive salary
• 25 days annual leave
• Private Health Cover via Bupa
• Cycle-to-Work Scheme
• Subsidised Nursery scheme
• Hybrid working (2 days from home)

Commitment:

We are committed to equality of opportunity for all staff and applications from individuals are encouraged regardless of age, disability, sex, gender reassignment, sexual orientation, pregnancy and maternity, race, religion or belief and marriage and civil partnerships.

Please note that all offers of employment are conditional on us obtaining satisfactory pre-employment checks, including a DBS check, a credit check and employment references.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Finance and Sales

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