Join our dynamic IT team on a mission to revolutionise data delivery worldwide! We emphasize simplicity, mobility, and efficiency, with data and analytics at the heart of enhancing customer experiences and optimizing business processes through innovative solutions. *This role is a hybrid role – 3 days per week in our Newcastle Office* Role Overview: As a Data Scientist, reporting to the BI and Analytics Manager, youll be a pivotal member of our BI and Analytics Hub. Youll develop advanced analytics and machine learning models to transform our understanding and prediction of customer behaviour. Using cutting-edge methodologies and big data technologies, youll bridge business needs and technical solutions, fostering close collaboration across the organization. Your work will ensure our data-driven solutions are robust, scalable, and impactful. Key Responsibilities Key Contributions: • Deliver data solutions and services that optimize customer connections across channels. • Transform our complex IT data estate by unifying disparate data sources into a single, managed version of the truth. • Ensure data integrity through central data mastering and modelling, enabling colleagues to interact with data to meet their needs. • Simplify data integrations between systems via a central platform, enhancing user experience and minimizing risk. • Promote a culture of data-driven experimentation, showcasing the value of our data through insights and analytics, and demonstrating emerging tech tools. Key Responsibilities: • Develop and own data science solutions, applying statistical/machine-learning models for segmentation, classification, optimisation, and time series analysis. • Present findings to the wider team and organisation. • Identify insights and suggest recommendations to influence business direction. • Develop and optimise churn prediction models to understand customer retention patterns and implement mitigation strategies. • Build forecasting models to predict business KPIs, customer lifetime value, and revenue trends using machine learning and statistical techniques. • Integrate Large Language Models (LLMs) into RAG-based systems to improve knowledge retrieval and decision support for enterprise applications. • Collaborate with data engineers to design scalable data pipelines for machine learning model deployment and inference at scale. • Work with cross-functional teams to translate business problems into data science solutions. • Develop ETL processes and data transformation workflows for structured and unstructured data. • Utilise big data technologies like Spark and Snowflake to process, store, and analyse large datasets efficiently. • Optimise and fine-tune LLMs to improve their performance within RAG systems and ensure alignment with business goals. • Perform A/B testing and statistical analyses to validate model effectiveness and recommend improvements. • Communicate findings and insights to stakeholders through compelling data visualizations and presentations. Skills, Know-How, and Experience: • Strong proficiency in Python (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow) and SQL. • Experience with big data frameworks such as Apache Spark, Databricks, or Dask. • Hands-on experience with cloud platforms like AWS (S3, Lambda, SageMaker, Redshift), Azure, or GCP. • Knowledge of Snowflake, including Snowpark for scalable data processing and ML integration. • Familiarity with MLOps principles, CI/CD pipelines, and model deployment in production environments. • Knowledge of NLP techniques and experience with transformer-based LLMs (e.g., OpenAI, Llama, Claude). • Strong understanding of machine learning algorithms for classification, regression, clustering, and time series forecasting. • Experience with data visualisation tools such as Tableau, Power BI, or Python-based libraries (Matplotlib, Seaborn, Plotly). • Excellent problem-solving skills, analytical thinking, and ability to communicate complex technical concepts to non-technical stakeholders. • Experience in customer analytics, digital marketing, or e-commerce industries. • Familiarity with vector databases and embedding-based retrieval techniques for RAG implementations. • Familiarity with modern agentic AI techniques eg Model Context Protocol (MCP) Technical/Professional Qualifications: • Degree in a quantitative discipline (applied mathematics, statistics, computer science, operations research, or related field). • Demonstrable experience in exploratory data analysis and feature engineering. • Experience with Python, Scikit-learn, PyTorch. Ideally, experience with PySpark, Snowflake, AWS, and GitHub (MLOps practices). Ready to make a difference with your data science expertise? Apply now and be part of our innovative journey