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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Specialist - Data Scientist - **Company:** Morrisons - **Location:** Bradford, UK - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Java (Programming Language), A/B Testing, Agile Methodology, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, BigQuery, C++ (Programming Language), Cloud Computing, Computer Programming, Data Governance, Data Presentation, Statistical Hypothesis Testing, Python (Programming Language), Logistic Regression, Machine Learning, Natural Language Processing, Recommender Systems, Standard Sql, Software Engineering, SQL Databases, Unstructured Data, Google Cloud, Sql Optimization, Deep Learning, Code Structure, Information Technology, Looker Analytics - **Published:** September 10, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=b7622f45d31403ed ## About the Role * Programming & Development: Strong programming capabilities in Python or R (or transferable languages such as SQL, Java, C++) with proficiency in writing clean, tested, and maintainable code. * Applied Maths & Statistics: Deep knowledge of descriptive and inferential statistics, hypothesis testing, univariate analysis, and probability distributions. * Machine Learning & Predictive Modelling: Experience building, validating, and productionising machine learning models (e.g., linear/logistic regression, time-series forecasting, decision trees). * Advanced SQL: Excellent SQL skills to extract, transform, and profile large-scale datasets from cloud data marts. * Causal Inference & Experimentation: Understanding of experimental design and causal inference methods to measure true trial interventions versus background trends. * Data Storytelling & Visualisation: Ability to translate complex algorithmic outputs into clear visual stories, dashboards (e.g., Looker), and executive recommendations. * Data Ethics & Privacy: Understanding of ethical data usage, privacy standards, and bias mitigation in statistical modelling. * Delivery Frameworks: Practical experience working within Agile or Waterfall delivery methodologies and scoping Minimum Viable Products (MVPs). Desirable: * Cloud Platforms: Hands-on experience deploying data science solutions on Google Cloud Platform (BigQuery, Vertex AI, GCP storage) or AWS/Azure. * Advanced Data Science Techniques: Exposure to advanced methods such as Natural Language Processing (NLP), deep learning, or mathematical optimisation. * Academic Background: Degree or equivalent background in Mathematics, Physics, Statistics, Engineering, Computer Science, or a related quantitative field. * Domain Experience: Experience working within FMCG, retail, manufacturing, supply chain, or logistics environments. ## Description * Build trusted relationships with internal stakeholders to understand operational context and business priorities. * Solve complex business problems using applied mathematics, machine learning, and statistical modelling. * Own data science deliverables over the full life of data products, providing clear updates and presenting complex statistical outputs in accessible formats. * Where required, develop agile proofs-of-concept (POCs) to showcase potential solutions to stakeholders. Colleague * Champion data literacy and the benefits of data science approaches across the organisation. * Mentor squad members in technical concepts such as programming, code structure, and statistical validation. * Embody a fast, value-driven turnaround culture for smaller analytical requests that unlock immediate operational impact. Cash & Profit * Focus all modelling efforts on driving commercial returns, supporting EBITDA improvement through sales growth, waste reduction, and efficiency gains. * Maintain a lean working approach by writing efficient, scalable code that minimises cloud compute overheads in GCP. Operational * Extract, manipulate, profile, and transform structured and unstructured data using state-of-the-art facilities in Google Cloud Platform. * Design, build, test, and productionise machine learning models (e.g., sales and waste forecasting, customer segmentation, recommendation engines). * Build feature sets and establish reusable data science pipelines and validation checks. * Apply causal inference and experimental frameworks (A/B testing) to isolate true business impacts from baseline operational noise. * Uphold strict compliance with data governance, privacy legislation, and ethical AI frameworks. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Making Data Warehouses fast. 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