Senior Machine Learning Data Scientist - Credit Risk

Senior Machine Learning Data Scientist - Credit Risk

£80,000-£120,000 + Bonus + Benefits (Flexibility to £150k DOE)

East Midlands (Hybrid Min. 3 days)

The Opportunity

There are very few roles in the UK market where you can take ownership of a proven, production-grade credit risk model that is already outperforming competitors - and be given the autonomy to evolve it, refine it and directly influence commercial outcomes.

This is one of them.

This opportunity sits within a high-growth, data-driven financial services environment where machine learning is not theoretical or exploratory - it is embedded at the core of how the business makes decisions.

At the centre of this capability is a highly accurate credit risk model, supported by rich, real-world datasets and a continuous feedback loop of internal and external lending outcomes. The model is already delivering strong predictive performance, but the real value lies in how it is developed from here.

This opportunity represents a natural evolution of an already successful machine learning capability, offering the chance to take ownership of a proven model and shape its future direction.

The Role

This is a senior, hands-on data science role focused on credit risk modelling within a commercial lending environment.

You will take ownership of the core modelling framework, working directly on probability of default models and broader decisioning logic that underpins lending strategy. The emphasis is on refinement, optimisation and continuous improvement rather than building from scratch.

You will be responsible for the intellectual core of the models:

Feature engineering across financial, behavioural and transactional data

Algorithm selection and tuning (logistic regression, gradient boosting, ensemble methods)

Model validation, performance optimisation and ongoing recalibration

Ensuring models remain robust in changing economic conditions You will not be responsible for infrastructure, pipelines or deployment. A dedicated engineering team manages AWS and production environments, allowing you to focus on modelling and analytics.

This is a highly visible role with direct exposure to senior stakeholders. You will be expected to explain model performance, justify modelling decisions and translate technical outputs into clear commercial insight.

The Environment

This is a business that understands the value of data but is still at a stage where impact is direct and visible.

There is:

No large data science hierarchy

No separation between thinking and execution

No dilution of responsibility across multiple teams You will operate as the central subject matter expert within a collaborative technical environment, with the autonomy to influence both modelling direction and commercial outcomes.

Over time, there is a clear pathway to build out a team and evolve into a leadership role. However, the immediate focus is on hands-on ownership and delivery.

What This Role Is Not

This role will not suit individuals who:

Have moved fully into leadership and no longer build models themselves

Prefer purely strategic or advisory positions without technical ownership

Are focused on infrastructure, MLOps or engineering rather than modelling

Want a large team or established function around them from day one This is a role for someone who wants to remain close to the detail and take responsibility for outcomes.

The Ideal Profile

You are a hands-on machine learning data scientist with deep experience in credit risk modelling.

You are currently building, refining and optimising models yourself, not delegating that work.

You are likely to have developed your career within:

SME lending, fintech or banking environments

Credit risk, underwriting or decision science functions You will have:

Strong experience building probability of default or credit scoring models

Advanced Python capability

Experience with algorithms such as logistic regression, XGBoost, LightGBM or similar

A strong understanding of model evaluation (ROC-AUC, Gini, precision/recall)

Experience working with complex financial or behavioural datasets You understand how your work impacts:

Approval rates

Default risk

Commercial performance You are comfortable discussing modelling decisions in depth with technical stakeholders, but equally able to simplify complex concepts for non-technical audiences.

Qualifications

You will typically have a strong academic foundation in a quantitative discipline such as Mathematics, Statistics, Data Science, Engineering, Physics or a closely related field.

Many candidates at this level will hold a Master's degree or equivalent advanced qualification, although this is not essential where there is clear evidence of deep practical expertise in credit risk modelling and machine learning.

What is critical is a strong grounding in mathematical thinking, statistical modelling and problem solving, combined with the ability to apply that knowledge in a commercial environment.

Why This Role Stands Out

Ownership of a high-performing, production-grade credit risk model

Access to rich, real-world data with continuous feedback loops

Direct influence on lending decisions and commercial performance

Strong engineering support, allowing full focus on modelling

High visibility with senior leadership

Clear pathway to future Head of AI / Machine Learning role

Opportunity to shape the next phase of a proven data capability

Package & Flexibility

£80,000-£120,000 base salary

Bonus up to 15%

Flexibility to £150,000 for exceptional candidates

Hybrid working (East Midlands, typically 2-3 days onsite with flexibility)