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

1. GT interview with Recruiter

2. Technical interview

3. Cultural fit interview

4. Final interview

5. Reference check

6. Security check