Senior Data Scientist - Supply Chain Optimization (Contract)

Harnham - Data & Analytics Recruitment; City Of London; Hybrid; GBP 92,000 - 111,000; Full time

Benefits offered by this job

Hybrid work model

London office attendance

Job summary

Harnham - Data & Analytics Recruitment is seeking a Senior Data Scientist to join a large-scale supply chain optimisation programme in London. You will design, develop and enhance optimisation models to support operational decisions across a vast network, leveraging strong OR and mathematical modelling skills in a highly data-driven environment.

The role focuses on real-world optimisation rather than generative AI or NLP, with a long-term strategic programme and a hybrid London office presence.

Qualifications

• Strong background in optimisation, OR, or mathematical modelling.
• Experience solving real-world supply chain, logistics, inventory, or network optimisation problems.
• Hands-on with optimisation platforms (AIMMS, AMPL, Gurobi, CPLEX, FICO Xpress) and Python.

Responsibilities

• Architect and design optimisation models for complex logistics and supply chain challenges.
• Improve and scale existing optimisation solutions.
• Translate business problems into mathematical models with stakeholders.
• Deliver solutions handling billions of potential decision combinations.
• Support ongoing development of a critical optimisation platform used across the organisation.

Skills

Optimization

Operational Research

Mathematical Modelling

Python

Education

Mathematics / OR background

Tools

AIMMS

AMPL

Gurobi

CPLEX

FICO Xpress

Job description

Rate: GBP500-600/day Outside IR35

Duration: Initial 6-Month Contract (Extension Likely)

Location: London (Hybrid)

Office Attendance: 2 days per week in the London office.

The Opportunity

We're supporting a major UK retailer on a large-scale supply chain optimisation programme and are looking for a Senior Data Scientist with a strong background in Operational Research and optimisation.

This is not a Generative AI, NLP, or customer analytics role. Instead, you'll be working on highly complex optimisation challenges that drive real-world operational decisions across a large supply chain network.

The programme has been running for several years and is viewed as a long-term strategic initiative, with further investment already secured.

The Role

You'll be responsible for designing, developing and enhancing optimisation models that support large-scale supply chain decision-making.

Key Responsibilities

• Architecting and designing optimisation models for complex logistics and supply chain challenges
• Improving and scaling existing optimisation solutions
• Working closely with stakeholders to translate business problems into mathematical models
• Delivering solutions capable of handling billions of potential decision combinations
• Supporting the ongoing development of a critical optimisation platform used across the organisation

Required Experience

We're particularly interested in candidates who can demonstrate:

• Strong experience in optimisation, Operational Research, or mathematical modelling
• Experience solving real-world supply chain, logistics, inventory, transportation, or network optimisation problems
• Hands-on experience with optimisation platforms such as AIMMS, AMPL, Gurobi, CPLEX, FICO Xpress, or similar
• Strong Python skills
• Experience working with large-scale optimisation problems where solution quality and performance are critical
• A strong academic background in Mathematics, Operational Research, Statistics, Engineering, Computer Science, or a related discipline

Ideal Background

Suitable candidates may come from:

• Retail
• Logistics
• Transportation
• Manufacturing
• Supply Chain Technology
• Warehousing & Distribution
• Airline Operations
• E-commerce Fulfilment

A PhD is not essential, but candidates will typically possess strong mathematical and analytical depth. Those with PhD research linked to real-world optimisation problems are highly encouraged to apply.

What They’re Looking For

Beyond technical expertise, the team values individuals who are:

• Naturally curious
• Proactive problem solvers
• Comfortable challenging assumptions
• Strong communicators who ask the right questions
• Passionate about solving complex operational problems

Interview Process

• 1-2 stage interview process
• Quick turnaround on feedback
• Immediate requirement