Data Scientist (Contract)

Harnham
London, UK
11 days ago
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Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Compensation
£104,000.0 - £130,000.0
Working hours
Regular working hours
Job source

Tech stack

Big Data Python (Programming Language) Machine Learning SQL Databases Data Analytics

Job description

London 2 Days in Office Outside IR35 £400 - £500 Per Day 6 months long The Company They are a well-established consumer goods business with a strong presence across multiple markets. The organisation is investing heavily in data and analytics to improve customer understanding, optimise commercial performance, and support strategic decision-making. You will join a collaborative team that values innovation, curiosity, and evidence-based thinking. The Role and Deliverables

  • Develop and deploy data science solutions to address customer, marketing, and commercial challenges.
  • Build predictive models to support demand forecasting, customer behaviour analysis, and business planning.
  • Analyse large datasets to identify trends, opportunities, and actionable insights.
  • Partner with stakeholders across commercial, marketing, and analytics functions to translate business requirements into data-driven solutions.
  • Present findings and recommendations to both technical and non-technical audiences.
  • Support the continuous improvement of analytics methodologies, tools, and processes.

Requirements

  • Strong experience applying data science techniques within a consumer goods, retail, or customer-focused environment.
  • Demonstrated capability in machine learning, statistical modelling, and predictive analytics.
  • Proficiency with Python, SQL, and relevant data science libraries.
  • Experience working with large and complex datasets to generate business value.
  • Ability to communicate technical findings clearly to senior stakeholders.
  • Experience delivering end-to-end data science projects from problem definition through to implementation.
  • Strong commercial awareness and a data-driven approach to decision-making.

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