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# Product Data Scientist

- **Company:** Harnham
- **Location:** Sheffield, UK
- **Salary:** £50,000.0 - £60,000.0
- **Contract:** Permanent contract
- **Skills:** Cloud Database, Information Engineering, Data Infrastructure, Python (Programming Language), Machine Learning, Power BI, Tableau (Software), Usage Analysis, Sql Optimization, Snowflake, Tools for Reporting, Looker Analytics
- **Published:** September 22, 2026
- **Apply:** https://www.reed.co.uk/jobs/product-data-scientist/57377099

## About the Role

If you're passionate about turning complex data into actionable business insights, this is an opportunity to make a tangible impact within a growing technology-led organisation. You'll combine analytical discovery, predictive modelling, and stakeholder collaboration to shape product strategy, improve customer outcomes, and influence key business decisions., The successful Product Data Scientist will have the following skills and experience:

 * Strong commercial experience in Data Science, Product Analytics, or Advanced Analytics environments.
 * Advanced SQL skills with experience querying complex cloud-based data warehouses, ideally including Snowflake.
 * Expertise in Python or R for statistical analysis, machine learning, and predictive modelling.
 * Experience analysing customer journeys, retention, lifecycle metrics, and behavioural data.
 * Ability to communicate technical findings clearly to both technical and non-technical stakeholders.
 * Experience with BI and visualisation tools such as Power BI, Tableau, Looker, or similar.
 * Familiarity with dbt and modern analytics engineering practices is highly desirable.

## Description

As a Product Data Scientist, you will drive product strategy and customer growth through advanced analytics, customer journey insights, and predictive data science in a modern cloud data environment.

Specifically, you can expect to be involved in the following:

 * Analyse end-to-end customer and user journeys to identify adoption trends, engagement drivers, and opportunities for growth.
 * Conduct exploratory analysis to uncover product usage patterns, conversion drop-offs, and areas for optimisation.
 * Build predictive models focused on areas such as customer churn, engagement scoring, demand forecasting, and propensity modelling.
 * Develop customer segmentation and behavioural cohorts to support targeted business initiatives.
 * Translate complex business questions into structured analytical frameworks, hypotheses, and actionable recommendations.
 * Partner with data engineering teams to ensure analytical requirements are embedded within the wider data platform.
 * Design dashboards, reporting solutions, and KPI frameworks that support executive and operational decision-making.


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