Senior Data Scientist

Senior Data Scientist

Manchester (Hybrid – 2 days onsite)

Up to £85,000 + bonus and Benefits

The Opportunity

This is a high-impact role within a growing SaaS organisation building a next-generation decisioning platform used by enterprise customers across multiple markets.

You’ll be joining at a pivotal moment — moving from project-based analytics to a product-led intelligence capability, where data science directly shapes commercial outcomes, not just dashboards.

The core focus is a recommendation and optimisation engine — a product that enables users to make high-value, high-frequency decisions with confidence. Getting the models right — and proving they work — is critical.

If you’ve ever been frustrated building models that never make it into production, this is the opposite environment.

What You’ll Be Doing

• Designing and deploying production-grade machine learning models that directly influence commercial decisions
• Building recommendation and optimisation systems across pricing, segmentation, and behavioural modelling
• Developing measurement frameworks to prove real-world impact (not just theoretical accuracy)
• Creating explainable outputs that non-technical users trust and act on
• Working closely with Product, Engineering, and Data to ensure models land and drive outcomes
• Acting as a senior voice within the team — raising standards, reviewing work, and shaping best practice

What We’re Looking For

• Strong experience in machine learning / statistical modelling in production environments
• Proven track record of building models that are deployed, monitored, and used
• Experience working in commercial / product-led environments (not just research or analysis)
• Ability to operate with autonomy — breaking down problems and delivering at pace
• Strong communication — able to translate complex outputs into clear business impact

Nice to have:

• Experience with pricing, optimisation, or recommendation systems
• Familiarity with explainable AI techniques (e.g. SHAP, feature importance)
• Exposure to MLOps / model lifecycle tooling (Databricks, MLflow, etc.)

The Environment

• Modern data stack and tooling (lakehouse architecture, ML pipelines, AI-assisted development)
• Product-led culture — models are expected to ship and deliver impact
• Close collaboration between Data, Product, and Engineering
• Backed by strong investment and a clear roadmap

Why Join

• Work on genuinely complex, high-value problems
• Build models that are actually used day-to-day
• Join a team at an inflection point — where you can shape direction, not just contribute
• Clear progression as the function scales