Data Science Manager - Operational Research

Data Science Manager - Operational Research & Optimisation
Location:

Hybrid (1 day a week London)
Salary:

£80,000-£110,000+ £6K Car Allowance + 10% Bonus + Excellent Benefits

We’re recruiting on behalf of one of the UK’s largest and most influential automotive groups for a

Data Science Manager - Operational Research & Optimisation . This is an outstanding opportunity for an experienced data scientist and people leader to shape operational strategy using cutting-edge optimisation techniques across a high-impact, data-rich organisation.

With a remit covering everything from

vehicle logistics

to

refurbishment optimisation , this role is perfect for someone who’s passionate about delivering measurable business value through advanced modelling and hands-on leadership.

The Role
As the

Data Science Manager

for the

Operations team , you will lead a growing group of data scientists focused on solving real-world operational challenges. Your team will design and deploy advanced models, applying advanced mathematical optimisation techniques (e.g.,

Linear Programming ,

Scheduling ,

Graph Theory ) to complex business problems, with the goal of improving supply chain efficiency, optimise vehicle movement, and enhance operational workflows across the organisation.

You’ll be supported by a modern MLOps and Data Engineering function, giving you the time and tools to focus on

innovation ,

model development , and

strategic delivery .

Key Responsibilities
Lead and mentor a data science team focused on operational optimisation, logistics, and refurbishment strategy.
Define and deliver a product roadmap that solves key operational pain points through data science and algorithmic innovation.
Apply advanced mathematical optimisation techniques (e.g.,

Linear Programming ,

Scheduling ,

Graph Theory ) to complex business problems.
Work cross-functionally with senior stakeholders to translate business requirements into scalable technical solutions.
Collaborate with MLOps and Engineering teams to productionise models using robust and scalable pipelines.
Champion the integration of model outputs into wider data and reporting platforms.
Clearly communicate technical insights and model outcomes to non-technical stakeholders across all levels.

Your Background & Skills
Required:
Proven experience building optimisation models using Python libraries such as

PuLP ,

ortools , or

SciPy.optimize .
Hands-on expertise in

combinatorial optimisation ,

scheduling algorithms ,

network optimisation , and/or

simulation methods

(e.g., Monte Carlo, Markov chains).
Strong track record of managing and growing high-performing data science teams.
Excellent stakeholder management and communication skills – able to explain complex concepts in accessible language.
Proficiency in tools such as

Azure ML Studio ,

Databricks ,

AWS/SageMaker ,

Snowflake , and cloud-native platforms.
Familiarity with CI/CD tools like

Azure DevOps Pipelines

or

GitHub Actions .
Comfortable working in Agile environments and contributing to iterative product development.

Bonus if you have:
Experience integrating models into operational decision-making processes or logistics platforms.
Exposure to Agile delivery methodologies or working in cross-functional squads.

What You’ll Get in Return
A leadership role where your work has

direct and measurable impact

on operational efficiency and bottom-line performance.
Dedicated support from MLOps and Engineering teams to accelerate delivery.
Access to career development support including

coaching, mentoring, and leadership training .
A competitive salary package including

car allowance ,

bonus , and

comprehensive benefits

such as enhanced parental leave, pension scheme, and mental health support.
The chance to join a forward-thinking group of businesses that are reshaping the automotive industry with technology and data at the core.

Ready to lead a high-performing team where operational data science meets real-world impact?

Apply today

or reach out for a confidential discussion.