At a Glance
- Tasks: Lead a data science team to solve operational challenges using advanced optimisation techniques.
- Company: Join one of the UK's largest automotive groups, driving innovation in the industry.
- Benefits: Enjoy a competitive salary, car allowance, bonus, and excellent benefits including mental health support.
- Why this job: Make a real impact on operational efficiency while working with cutting-edge technology and data.
- Qualifications: Experience in optimisation models, Python, and managing high-performing teams is essential.
- Other info: Hybrid work model with dedicated support from MLOps and Engineering teams.
The predicted salary is between 64000 - 88000 £ per year.
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.
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, optimising vehicle movement, and enhancing 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.
Data Science Manager - Operational Research employer: KDR Talent Solutions
Contact Detail:
KDR Talent Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Science Manager - Operational Research
✨Tip Number 1
Familiarise yourself with the specific optimisation techniques mentioned in the job description, such as Linear Programming and Graph Theory. Being able to discuss these concepts confidently during interviews will demonstrate your expertise and alignment with the role.
✨Tip Number 2
Showcase your leadership experience by preparing examples of how you've successfully managed and grown data science teams in the past. Highlighting your ability to mentor others will be crucial for this managerial position.
✨Tip Number 3
Network with professionals in the automotive and data science sectors. Engaging with industry events or online forums can provide insights into current trends and challenges, which you can reference in your discussions with potential employers.
✨Tip Number 4
Prepare to discuss how you've integrated data science models into operational decision-making processes. Real-world examples will help illustrate your capability to translate complex data insights into actionable business strategies.
We think you need these skills to ace Data Science Manager - Operational Research
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data science and operational research. Emphasise your expertise in optimisation techniques and any leadership roles you've held, as these are crucial for the Data Science Manager position.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for data science and operational optimisation. Discuss specific projects where you've successfully applied advanced mathematical techniques and how you can bring value to the company.
Highlight Technical Skills: Clearly list your proficiency in Python libraries like PuLP and SciPy.optimize, as well as your experience with tools such as Azure ML Studio and AWS/SageMaker. This will demonstrate your technical capability to potential employers.
Showcase Leadership Experience: Since this role involves leading a team, include examples of how you've managed and mentored data science teams in the past. Highlight your stakeholder management skills and ability to communicate complex concepts effectively.
How to prepare for a job interview at KDR Talent Solutions
✨Showcase Your Technical Skills
Be prepared to discuss your experience with optimisation models and Python libraries like PuLP or SciPy.optimize. Bring examples of past projects where you applied advanced mathematical techniques to solve complex problems.
✨Demonstrate Leadership Experience
Highlight your experience in managing and mentoring data science teams. Share specific instances where you successfully led a team to achieve operational goals, focusing on how you fostered collaboration and innovation.
✨Communicate Clearly with Stakeholders
Practice explaining complex technical concepts in simple terms. Be ready to discuss how you've effectively communicated insights to non-technical stakeholders in previous roles, as this is crucial for the position.
✨Familiarise Yourself with Agile Methodologies
Since the role involves working in Agile environments, brush up on Agile principles and be ready to discuss your experience with iterative product development. Mention any tools you've used, like Azure DevOps or GitHub Actions.