Senior Data Scientist Decisioning & Pricing Intelligence

Manchester (Hybrid 2 days onsite)
Up to £90,000 + bonus

The Opportunity

This is not a role where you sit in a notebook building models no one uses.

Our client is a market-leading SaaS organisation operating at scale within the automotive ecosystem, building the intelligence layer that drives real-world commercial decisions across Europe.

They are investing heavily in data and AI to power a next-generation decisioning platform helping enterprise clients make high-value pricing, trading, and operational decisions across thousands of assets, in real time.

?? This is about building models that directly influence revenue, pricing, and market behaviour.

The Role

Youll take ownership of production-grade models that sit at the core of a live product used daily by commercial teams across multiple markets.

This includes:

• Pricing intelligence
• Buyer behaviour modelling
• Stock segmentation
• Recommendation systems

Youll work closely with product, engineering, and domain experts to ensure your work doesnt just function it lands, scales, and delivers measurable impact.

What Youll Be Doing

• Building and deploying machine learning models into production environments
• Designing decisioning systems that optimise pricing, channel selection, and asset performance
• Developing explainable models (SHAP, LIME, feature importance) that drive user trust
• Creating frameworks to measure real-world impact and model effectiveness
• Working with large, complex datasets across multiple markets
• Collaborating with product and engineering teams to embed models into live systems

What Were Looking For

• Strong experience in machine learning, statistical modelling, or pricing / propensity modelling
• Proven track record of delivering models that go into production and stay there
• Experience working in commercial environments where data drives decisions
• Strong Python (or R) experience across modern data science tooling
• Familiarity with MLOps, model monitoring, and deployment pipelines
• Ability to communicate complex outputs clearly to non-technical stakeholders