Senior Data Scientist / ML Engineer (Forecasting) | NDA

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

Requirements

• 4+ years of commercial experience in Data Science / Machine Learning
• Fluent English

Preferred

• Retail or similar consumer-facing industry experience

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.

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)

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