Data Scientist - FMCG, Distribution & Operations
Harnham - Data & Analytics Recruitment · London · posted 2 days ago
Occupation
Going rate for this occupation: £34,900 · UK median pay £38,572
Home Office going rates from
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Sponsorship chance
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Why
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Data Scientist
£400 per day | Outside IR35 | Six months | Remote, UK-based
We are working with a specialist Data Science consultancy that is looking for a Data Scientist to join a major engagement with a global FMCG business.
The role will focus on distribution and operations, using Data Science to improve areas such as logistics performance, forecasting, territory planning and operational decision-making.
This is a strong opportunity for a Data Scientist with three or more years of experience who has built solid technical foundations and is ready to take greater ownership of their work. You do not need to be the finished article, but you must be able to work independently, communicate confidently and apply Data Science to genuine business problems.
Working pattern
The role will be completed remotely from the UK. However, the end client's main stakeholders are based on the US West Coast.
Candidates must be comfortable adjusting their working hours and remaining available until approximately 7pm UK time. This will provide at least two hours of daily overlap with the US team.
The company
The consultancy specialises in solving complex commercial problems through Data Science and advanced analytics.
You will join a small, experienced delivery team supporting an established global consumer goods business. The working environment is collaborative and fast-moving, with the opportunity to contribute to how analytical models, processes and products are developed.
The consultancy places significant value on attitude and personality. They are looking for someone who is approachable, proactive and able to build trusted relationships with technical and non-technical stakeholders.
The role
You will work across the full analytical lifecycle, including:
Working with stakeholders across Sales, Operations and Distribution to understand their challenges.
Turning loosely defined business problems into clear analytical plans.
Identifying, acquiring and assessing the quality of relevant data.
Using Python, SQL and Snowflake to explore and structure large datasets.
Building forecasting, classification, clustering, regression and optimisation models.
Developing repeatable analytical models and pipelines rather than one-off analyses.
Applying experimentation and causal inference methods to measure the impact of business decisions.
Connecting analytical outputs to measurable operational and commercial outcomes.
Information from public records, not immigration advice.