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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