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- Tasks: Tackle real-world optimisation challenges in a dynamic supply chain environment.
- Company: Join a leading UK retailer with a massive logistics network.
- Benefits: Competitive salary, hybrid working model, and training on cutting-edge tech.
- Why this job: Make a tangible impact on stock allocation and drive commercial decisions.
- Qualifications: Experience in data science and optimisation modelling is essential.
- Other info: Collaborative team culture with opportunities for professional growth.
The predicted salary is between 43200 - 72000 £ per year.
Do you want to work on real-world optimisation problems at national scale? Have you built mathematical models that directly drive commercial decisions? Are you ready to own and improve optimisation systems used across a complex supply chain? A leading UK retailer, with one of the country's largest logistics networks, is strengthening its Data Science function and is hiring a Data Scientist to work on critical supply chain and stock allocation problems. This is a permanent role with immediate impact, sitting within a senior DS team that partners closely with Product, Engineering, and MLOps.
The role focuses on designing, improving, and scaling optimisation models that determine how stock is allocated across stores to best meet demand and maximise profit, while respecting real-world constraints such as warehouse capacity, product size, and total inventory levels. You will work on a production optimisation platform used at scale, collaborating with engineers while owning the modelling and decision logic behind the system.
- Key responsibilities
- Architect and improve large-scale optimisation models for supply chain use cases
- Enhance existing stock allocation models used across the retail estate
- Work on optimisation problems including inventory, logistics routing, and scheduling
- Collaborate closely with Product, Engineering, MLE and MLOps teams
- Identify new optimisation opportunities and lead solutions end to end
- Clearly explain modelling choices, assumptions, and trade-offs to stakeholders
Key details
- Salary: £60k–£100k base
- Working model: Hybrid, officially 2 days/week, typically 1 day/month in office (London)
- Tech stack: Python, Azure, AIMMS, IBM CPLEX (training provided)
Interested? Please apply below.
Data Scientist - Operations Research employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - Operations Research
✨Tip Number 1
Network like a pro! Reach out to current employees on LinkedIn or attend industry meetups. We can’t stress enough how personal connections can give you the inside scoop on the role and even get your foot in the door.
✨Tip Number 2
Prepare for those interviews by brushing up on your optimisation models and real-world applications. We recommend practising common data science interview questions and being ready to discuss your past projects in detail.
✨Tip Number 3
Showcase your problem-solving skills! During interviews, be ready to tackle hypothetical scenarios related to supply chain optimisation. We love seeing candidates think critically and creatively about real-world challenges.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always looking for passionate individuals who want to make an impact in the data science field.
We think you need these skills to ace Data Scientist - Operations Research
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Data Scientist - Operations Research. Highlight your experience with optimisation models and any relevant projects that showcase your skills in supply chain management.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about solving real-world optimisation problems. Share specific examples of how you've driven commercial decisions through your mathematical models.
Showcase Collaboration Skills: Since this role involves working closely with Product, Engineering, and MLOps teams, mention any past experiences where you collaborated effectively with cross-functional teams. We love seeing teamwork in action!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity to make an impact in our data science function.
How to prepare for a job interview at Harnham
✨Know Your Optimisation Models
Make sure you brush up on the optimisation models you've worked with in the past. Be ready to discuss how you've built and improved these models, especially in relation to supply chain problems. Highlight any real-world applications and the impact they had on commercial decisions.
✨Understand the Tech Stack
Familiarise yourself with the technologies mentioned in the job description, like Python, Azure, AIMMS, and IBM CPLEX. Even if you haven't used them all, showing that you're eager to learn and adapt will impress your interviewers. Maybe even mention a project where you used similar tools!
✨Collaboration is Key
This role involves working closely with various teams, so be prepared to talk about your experience collaborating with Product, Engineering, and MLOps teams. Share specific examples of how you’ve successfully worked in cross-functional teams to solve complex problems.
✨Communicate Clearly
You’ll need to explain your modelling choices and assumptions to stakeholders. Practice articulating your thought process clearly and concisely. Use examples from your past work to demonstrate how you’ve effectively communicated complex ideas to non-technical audiences.
