Senior Data Scientist / ML Engineer (Forecasting) | NDA
Gt Hq · UK - Hybrid
What this role requires
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Job description
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.
Read the full description (57 more sections)Show less
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
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