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 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