Senior Data Scientist / ML Engineer (Forecasting) | NDA

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About the role

GT was founded in 2019 by a former Apple, Nest, and Google executive. GT’s mission is to connect the world’s best talent with product careers offered by high-growth companies in the UK, USA, Canada, Germany, and the Netherlands. Our clients operate in industries like healthcare, life sciences, fintech, retail, e-commerce, finance and many more - giving our team exposure to real-world, high-impact projects.   About the Role We’re looking for a Senior Data Scientist / ML Engineer to join a UK-based client in the healthcare and pharmacy domain. The role combines forecasting and machine learning with end-to-end ownership of solution delivery, from project discovery and stakeholder collaboration through model development, deployment, and productionisation.   Location : Nottingham, UK Office attendance : up to 3 days per week in the Nottingham office. Project duration : 6 months (with possible extension). Project Details : The project focuses on developing a forecasting solution for a large healthcare network. It uses historical clinic and marketing data to predict clinic usage and staffing needs, helping optimize scheduling and resource allocation. The goal is to build a scalable, data-driven platform that improves operational efficiency.   Responsibilities: • Design, train, and deploy ML models for time-series forecasting and related data tasks • Build and maintain data pipelines using cloud-native tools (AWS, GCP, or Azure) • Develop and optimize forecasting models (Prophet, ARIMA, LSTM, TimeGPT) • Collaborate with data, product, and cloud engineers to deliver reliable, scalable solutions • Participate in different stages of the project lifecycle - from discovery and PoC to production deployment, presenting your work to stakeholders • Work closely with business stakeholders and SMEs to gather requirements, shape solutions, and drive project discovery • Communicate modelling approaches, assumptions, and results to both technical and non-technical audiences   Essential knowledge, skills & experience (must-have): • 4+ years of commercial experience in Data Science / Machine Learning • Hands-on experience with: • Databricks • Notebooks • PySpark • Workflows • Deployment through Asset Bundles • Proven experience building, deploying, and maintaining production ML solutions • Broad experience across multiple ML domains, including: • Forecasting / Time-Series Modelling • Regression • Classification • Gradient Boosting models (e.g. XGBoost, LightGBM) • Strong Python skills (Pandas, NumPy, scikit-learn, PyTorch) • Experience with model evaluation, performance monitoring, and accuracy metrics • Version control (Git) • Experience working with cloud environments (Azure preferred, AWS/GCP also considered) • SQL • Fluent English   Nice-to-have: • Retail or similar consumer-facing industry experience • Azure DevOps: • Repos • Boards • Pipelines • Experience with Databricks model training and inference workflows • Databricks Apps and Lakebase • Experience with RAG pipelines • Experience with vector databases (Weaviate, Milvus) • Familiarity with LLM evaluation frameworks (e.g. DeepEval)   Soft Skills • Strong sense of ownership and accountability • Strong stakeholder management skills • Proactive attitude and ability to work independently • Clear and confident communication with both tech and non-tech stakeholders • Comfortable working in ambiguity and helping define requirements • Strategic thinking and focus on business impact • Team player   Interview Steps • GT interview with Recruiter • Technical interview • Cultural fit interview • Final interview • Reference check • Security check Find Jobs in United Kingdom on Arbeitnow

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