Data Scientist in Newcastle upon Tyne

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, you'll be a pivotal member of our BI and Analytics Hub. You'll develop advanced analytics and machine learning models to transform our understanding and prediction of customer behaviour. Using cutting-edge methodologies and big data technologies, you'll 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:

• 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.
• 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.