Senior Data Scientist / ML Engineer (Forecasting) | NDA

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.

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: 1-2 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
• Final interview
• Reference check
• Security check