Principal Data Scientist

About the job

This role is being recruited for by Vecta, on behalf of one of our partner companies.

Principal Data Scientist - Credit Risk, Valuation & Data Strategy

Location: Northampton, UK (Hybrid)

Contract: Permanent, Full-Time

Salary: Competitive Base + Annual Bonus (up to 15%)

Reports To: Senior Leadership / Director Level

The Opportunity

A privately owned, high-growth UK financial services group - one that has funded over £1.5 billion for 20,000+ SMEs - is looking for a Principal Data Scientist to take ownership of the models and data science capability that sit at the heart of its lending operations.

The business operates as both a direct lender and a broker with a panel of 60+ lending partners. Credit decisions, asset valuations, and portfolio management underpin everything it does - and the company is investing heavily in AI and machine learning to sharpen these capabilities. This role is central to that investment.

As Principal Data Scientist, you will be the most senior hands-on practitioner in the team. Your primary focus will be designing, building, and refining the credit risk and valuation models that drive real lending decisions worth hundreds of millions of pounds. You’ll work end-to-end - from exploratory analysis and feature engineering through to validated, production-ready models deployed in AWS.

Critically, this is not a back-room role. You will regularly present model outputs, strategic recommendations, and performance insights to directors and senior leadership. The ability to translate complex technical work into clear, compelling narratives for non-technical decision-makers is essential.

If you want a role where your models go live, your recommendations shift real lending policy, and you have a direct line to the people running the business - this is it.

What You’ll Do

Model Development & Credit Risk

• Design and develop end-to-end machine learning models for credit risk - including probability of default, loss-given-default, exposure at default, and borrower scoring - from research through to production deployment.
• Build and refine discounted cash flow (DCF) models for asset valuation, integrating market data, historical performance, and business-specific signals.
• Propose and deliver improvements to existing credit risk models, credit strategies, and underwriting workflows - from scorecard refinement to new ML-driven decisioning.
• Design and run rigorous model validation and back-testing frameworks, ensuring models meet both internal standards and regulatory expectations.
• Establish model monitoring, drift detection, and retraining frameworks to keep models accurate and resilient in production.

Stakeholder Engagement & Presentation

• Present model performance, strategic recommendations, and data-driven insights to directors, senior leadership, and risk committees on a regular basis.
• Translate complex model outputs into clear, actionable narratives that non-technical stakeholders can use to make confident decisions.
• Build strong working relationships across underwriting, risk, operations, and commercial teams - acting as the bridge between data science and business strategy.
• Collaborate with senior leadership to quantify the business impact of AI initiatives and build the case for continued investment.

Data Strategy & Engineering

• Conduct deep exploratory data analysis to identify new predictive features, data quality issues, and opportunities to improve model accuracy.
• Partner with data engineering and technology teams to shape data architecture, feature stores, and clean, reliable pipelines that support ML workloads.
• Drive adoption of ML engineering best practices: reproducible pipelines, version control, testing, documentation, and automated retraining.
• Explore new data science applications across the business - from automated underwriting signals and portfolio segmentation to collections optimisation.
• Stay current with developments in credit risk modelling, applied ML, and relevant financial regulation, bringing external best practice into the organisation.

What You’ll Bring

Essential

• Proven, hands-on experience building credit risk models (PD, LGD, EAD, scorecards, or equivalent) in a lending or financial services environment - this is non-negotiable.
• A track record of taking models from research through to production deployment in live lending or decisioning systems.
• Excellent presentation and communication skills - you are confident and credible presenting to directors, risk committees, and senior leadership audiences.
• Strong interpersonal skills and the ability to build trusted relationships across technical and non-technical teams at all levels.
• Advanced Python skills with strong data science fundamentals (pandas, scikit-learn, XGBoost, statsmodels, or similar).
• A rigorous, evidence-driven approach to model development with strong problem-solving ability.
• A commercial mindset - you instinctively connect model performance to business outcomes and know how to prioritise for impact.

Preferred

• Degree in a quantitative discipline: Mathematics, Statistics, Physics, Computer Science, Engineering, or similar.
• Understanding of financial concepts such as discounted cash flows, net present value, and yield curves.
• Hands-on experience with AWS services (SageMaker, S3, Glue, Step Functions, or equivalent cloud ML infrastructure).
• Experience with MLOps tooling (MLflow, Airflow, dbt, or equivalent).
• Familiarity with model risk management frameworks and regulatory expectations (e.g. SR 11-7, PRA model risk guidance).
• Experience mentoring or technically leading other data scientists.

The Kind of Person Who Thrives Here

• You’re as comfortable in a boardroom as you are in a Jupyter notebook - you can explain a model’s assumptions to a director and debug a pipeline the same afternoon.
• You’re commercially sharp and understand that the best model is the one that gets adopted and drives better decisions.
• You’re calm and organised under pressure, comfortable owning multiple workstreams simultaneously.
• You build trust quickly across functions - people want to work with you because you make their teams better.
• You’re proactive and curious - you don’t wait for a brief to explore an interesting signal in the data.
• You’re energised by building something new rather than maintaining the status quo.

Why This Role

📊 Models That Matter

Your work directly shapes lending decisions on a book worth hundreds of millions of pounds.

🎯 Direct Access to Leadership

Present regularly to directors and senior leadership - your voice shapes strategy.

💰 Competitive Package

Strong base salary plus annual bonus of up to 15%.

🏠 Hybrid Working

Northampton HQ with flexible hybrid arrangements.

🚀 High-Growth Business

A company that’s more than doubled in recent years with serious scale ambitions.

🔬 Full Lifecycle Ownership

End-to-end: from EDA and feature engineering to deployment, monitoring, and board-level reporting.

About the Company

This is a well-established, privately owned UK finance group headquartered in Northampton. Founded in 2007, the business provides SMEs with access to a comprehensive range of funding options - from asset finance and hire purchase to business loans and government-backed schemes. Operating as both a direct lender and a broker with a panel of 60+ partners, the company has arranged over £1.5 billion in funding for more than 20,000 businesses across every sector.

Highly rated by its customers (4.8 stars across 900+ reviews) and having more than doubled in size in recent years, the business is making its most significant investment yet in AI and machine learning. This role is the cornerstone of that investment - the person hired will define how the company uses data science to compete, grow, and make better lending decisions for the next decade.

Please note: as part of the recruitment process, a criminal records check and a credit history check will be carried out by an authorised third party.