GT
Senior Data Scientist / ML Engineer (Forecasting) | NDA
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About GT
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.
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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
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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
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