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Senior Data Scientist / ML Engineer (Forecasting) | NDA

GT

UK - Hybrid Remote Contract Posted yesterday
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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 1. GT interview with Recruiter 2. Technical interview 3. Cultural fit interview 4. Final interview 5. Reference check 6. Security check

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