Credit Risk Analyst / Data Scientist

Eden Smith Group; England; On-site; GBP 42,000 - 54,000; Full time

Job summary

Eden Smith Group is seeking a Credit Risk Analyst / Data Scientist for their Sevenoaks office. The role involves collaborating within a data science team to develop predictive models and support innovative credit risk solutions. Candidates should have strong analytical skills and a solid foundation in relevant numerical fields, with a willingness to learn. Offering hybrid work arrangements and exposure to real-world financial challenges, this position promises a dynamic and impactful career opportunity.

Benefits

Hybrid working available

On site parking

Scenic office location

Collaborative team environment

Qualifications

• 1 to 2 years of experience in a financial data-driven environment or relevant academic projects.
• Familiarity with modeling techniques like logistic regression or basic machine learning is preferred.
• A willingness to learn new tools and techniques.

Responsibilities

• Develop predictive models and support loss forecasting.
• Explore new data sources and improve modeling techniques.
• Collaborate across departments for model validation and reporting.

Skills

Mathematical ability

Problem solving

Attention to detail

Communication

Interest in data science applications

Education

Degree in Mathematics, Statistics, Data Science, Economics or Physics

Tools

Python

T SQL

AWS

Excel

Job description

Are you looking to leave your starter jobs and progress your career in Credit Risk & Data Science? Do you want to work in a fast paced environment where you can make an immediate impact?

*PLEASE NOTE* - All Candidates MUST be able to commute ON-SITE to Seven Oaks, Kent 3 days per week. Public Transport is limited so IDEALLY candidate will hold a full UK drivers licence and own car.

We are hiring a Credit Risk Analyst / Data Scientist to join a collaborative and innovative team that is shaping the future of credit risk modelling and forecasting. This is a fantastic opportunity to work closely with experienced professionals and gain hands on experience in predictive modelling, loss forecasting, and machine learning, all within a growing, tech enabled financial services organisation.

The Role

You will be part of a small, high impact data science team responsible for:

• Developing predictive models such as scorecards and machine learning models for customer acquisitions and collections
• Supporting loss forecasting for both new business and the existing portfolio
• Exploring new data sources and modelling techniques to improve performance and accuracy
• Working with tools like Python, T SQL, and Excel to manage data workflows and build solutions
• Collaborating across departments with teams in credit risk, finance, capital markets, and operations
• Monitoring model performance and contributing to regular validation and compliance reporting

What you'll need?

• A degree, or strong mathematical ability, in a numerate subject such as Mathematics, Statistics, Data Science, Economics, or Physics
• 1 to 2 years of experience in a Financial data driven environment, or strong academic project experience
• Familiarity with modelling techniques like logistic regression or basic machine learning
• A keen interest in data science and its applications in finance or risk
• Strong attention to detail and a problem solving mindset
• A confident communicator who can explain data insights to both technical and non technical audiences
• A willingness to learn. For example, experience in Python/R, AWS or model deployment would be great, but it is important that you could learn this

Why work for us?

• Work in a high growth, data first business combining fintech agility with financial service rigour
• Be part of a collaborative and forward thinking team where your input matters
• Gain exposure to real world business problems and end to end model development
• Hybrid working available, with regular team interaction and support
• On site parking and scenic office location in Sevenoaks (a driving licence is helpful due to limited public transport)