Senior Data Scientist / ML Engineer (Forecasting) - NDA

Gt Hq; Nottingham; Hybrid; GBP 65,000 - 95,000; Full time

Job summary

GT is seeking a Senior Data Scientist / ML Engineer for a UK-based healthcare project in Nottingham. You will own end-to-end delivery from discovery to deployment, designing forecasting solutions and ML models that optimize clinic staffing and scheduling.

The role emphasizes collaboration with business stakeholders, data and cloud engineers, and SMEs, with hybrid office attendance up to 3 days per week in the Nottingham office. The project lasts 6 months with possible extension.

Qualifications

• 4+ years of commercial experience in Data Science / Machine Learning.
• Experience building, deploying, and maintaining production ML solutions.
• Strong Python skills (Pandas, NumPy, scikit-learn, PyTorch).

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.

Skills

Data Science

Machine Learning

Time-Series Forecasting

Python

Pandas

NumPy

scikit-learn

PyTorch

XGBoost

LightGBM

Databricks

PySpark

Git

SQL

Azure

AWS

GCP

Tools

Databricks

Notebooks

PySpark

Asset Bundles

Databricks Apps

Lakebase

RAG pipelines

Weaviate

Milvus

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.

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