At a Glance
- Tasks: Deliver consultancy projects and develop credit scorecards for major lenders.
- Company: Global data and analytics firm with a strong culture and excellent staff retention.
- Benefits: Up to £75,000 salary, hybrid working, and strong career progression.
- Other info: Work on diverse projects across various industries with modern tech.
- Why this job: Join a dynamic team and make impactful lending decisions using cutting-edge technology.
- Qualifications: Experience in credit scorecard development and strong SQL skills.
The predicted salary is between 45000 - 55000 £ per year.
This global data, analytics and technology business helps organisations make smarter lending decisions through market-leading credit data, analytics and consultancy. Working with banks, lenders, telecoms and other major organisations, they develop analytical solutions that improve customer acquisition, credit decisions and portfolio performance. Having recently migrated to the cloud and invested heavily in Python, it's an exciting time to join a business known for its strong culture and excellent staff retention.
As an Associate Consultant within the Client Analytics team, you'll deliver consultancy projects for some of the UK's largest lenders and financial services organisations. Working directly with clients, you'll build credit scorecards, develop lending strategies and create statistical models using Credit Reference Agency (CRA) data to help businesses make better lending decisions.
This is a highly commercial, client-facing position where you'll own analytical projects from discovery through to delivery, presenting insights and recommendations to senior stakeholders while balancing technical excellence with commercial impact.
You'll be responsible for:
- Developing acquisition and behavioural credit scorecards using CRA data
- Building statistical and machine learning models including logistic and linear regression
- Designing and implementing credit strategies for new and existing customer portfolios
- Analysing large datasets using SQL and Python (or SAS)
- Delivering analytical consultancy projects for external clients across multiple industries
- Presenting technical findings and commercial recommendations to senior stakeholders
- Supporting clients to improve lending decisions through data-driven insights
The role covers the full modelling lifecycle, including sample design, performance analysis, reject analysis and strategy development.
You’ll ideally have:
- Experience developing credit scorecards within a credit risk environment
- Strong knowledge of Credit Reference Agency (CRA) data (Equifax, Experian or TransUnion)
- Credit risk experience across model development or strategy
- Strong SQL skills alongside Python or SAS (Python preferred)
- Experience with statistical modelling techniques such as logistic regression or machine learning
- Excellent communication skills with the ability to present to clients and senior stakeholders
Benefits include:
- Up to £75,000 salary
- Hybrid working (Monday-Thursday office, Fridays remote)
- Work with a wide range of clients across financial services, telecoms and beyond
- Opportunity to work on varied analytical consultancy projects rather than a single portfolio
- Modern cloud-based technology stack with increasing use of Python
- Strong career progression within a business known for long employee tenure
- Two-stage interview process with a fast turnaround
Associate Analytics Consultant in London employer: Harnham - Data and Analytics Recruitment
Harnham is an exceptional employer that fosters a dynamic and inclusive work culture, where innovation in AI and data analytics thrives. With a strong commitment to employee growth, you will have access to continuous learning opportunities and the chance to lead transformative projects in a vibrant London setting. The hybrid working model ensures a healthy work-life balance, making it an ideal place for professionals seeking meaningful and rewarding careers.
Contact Details:
Harnham - Data and Analytics Recruitment Recruitment Team
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