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.

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

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