Senior Data Scientist

Via; Greater London; Hybrid; GBP 140,000 - 170,000; Full time

Job summary

Via is partnering with a fast-growing consumer technology business in London, seeking an exceptional Senior Data Scientist with deep expertise in credit risk modelling. The role is hands-on from day one, designing and deploying models that influence commercial decisions, with progression to Head of Data Science within about 12 months.

You'll work in a hybrid setup, collaborating with Product, Engineering, Risk and Commercial teams, mentoring others, and exploring modern AI applications to drive

Qualifications

• Master's degree or PhD in STEM with strong academic record.
• 7+ years of professional Data Science or predictive modelling experience.
• Hands-on credit risk modelling experience in fintech/scale-up.
• Experience deploying predictive ML models into production.
• Advanced Python and SQL; experience with ML frameworks and MLOps.
• Experience in fast-paced scale-ups and influencing customer decisions, risk or profitability.
• Proven technical leadership and mentoring of Data Scientists.

Responsibilities

• Design, develop, deploy and optimise production ML models for credit risk and underwriting.
• Build predictive models for credit decisioning, default, customer behaviour, churn.
• Own end-to-end modelling lifecycle from problem definition to deployment and monitoring.
• Collaborate with Product, Engineering, Risk and Commercial teams to translate challenges into ML solutions.
• Take ownership of modelling approaches with performance, explainability and business impact.
• Lead complex Data Science projects while coding, experimenting and developing models.
• Mentor and guide other Data Scientists to establish technical standards.
• Shape Data Science roadmap and build the function as the business grows.
• Explore opportunities to apply modern AI, including LLMs and agentic AI, when valuable.

Skills

Credit risk modelling

Leadership

Python

SQL

Communication

Mentoring

Education

Master's or PhD in STEM

Tools

ML frameworks

MLOps

Job description

Salary: £140,000–£170,000 per year

Location: London, SE1

Working arrangement: Hybrid – minimum 4 days per week in the office

Employment type: Permanent, Full-time

About the opportunity

Via is partnering with a fast-growing consumer technology business looking for an exceptional Senior Data Scientist with deep expertise in credit risk modelling and the ambition to build and lead a Data Science function.

This is a hands-on technical role from day one. You'll initially operate as a Senior Data Scientist, personally developing and deploying machine learning models that influence real commercial decisions. As the business scales, you'll take increasing ownership of the Data Science function, with the opportunity to progress into Head of Data Science within approximately 12 months.

We're looking for someone who combines outstanding technical ability with proven leadership potential, ideally gained within a fast-growing fintech, consumer lending or credit-focused technology business.

What you'll be doing

• Personally design, develop, deploy and optimise production machine learning models, with a particular focus on credit risk and underwriting.
• Build predictive models supporting credit decisioning, default prediction, customer behaviour, churn and retention.
• Own the end-to-end modelling lifecycle, from problem definition and feature engineering through to deployment, monitoring and continuous improvement.
• Work closely with Product, Engineering, Risk and Commercial teams to translate business challenges into scalable ML solutions.
• Take technical ownership of modelling approaches, ensuring strong model performance, explainability and commercial impact.
• Lead complex Data Science projects while remaining actively involved in coding, experimentation and model development.
• Mentor and guide other Data Scientists, helping establish strong technical standards and best practices.
• Shape the Data Science roadmap and progressively build the function as the business grows.
• Explore opportunities to apply modern AI, including LLMs and agentic AI, where they deliver measurable value.

What we're looking for

• Master's degree or PhD in a STEM discipline from a leading university, with a strong academic record.
• At least 7 years of professional Data Science or predictive modelling experience.
• Proven hands-on credit risk modelling experience within a fintech, consumer lending, credit-based subscription or closely comparable scale-up environment.
• Strong experience developing and deploying predictive ML models into production, rather than solely overseeing modelling teams.
• Advanced Python and SQL skills, alongside experience with modern machine learning frameworks and MLOps practices.
• Experience working in a fast-paced scale-up, ideally developing models that directly influence customer acquisition, credit decisions, risk or profitability.
• Demonstrable technical leadership, including leading modelling projects, mentoring Data Scientists and making key technical decisions.
• The ambition and capability to build and lead a growing Data Science function while maintaining strong technical involvement.
• Excellent communication skills and the ability to influence technical and non-technical stakeholders.

Particularly valuable experience

• Credit scoring, probability of default, underwriting or affordability modelling.
• Customer churn, retention, lifetime value or propensity modelling.
• Consumer fintech, lending, subscription or other high-volume B2C product environments.
• Building Data Science capabilities in an early-stage or scaling technology company.
• Applying LLMs, generative AI or agentic AI in production environments.

Why consider this opportunity?

• Competitive salary of £140,000–£170,000.
• Clear progression from Senior Data Scientist into Head of Data Science as the team expands.
• Opportunity to own commercially important ML models from development through to production.
• Work in a growing consumer technology business where Data Science directly influences business performance.
• Shape the technical direction, hiring and development of a Data Science function.
• Combine meaningful hands-on technical work with increasing leadership responsibility.