Senior Data Scientist / ML Engineer (Forecasting) | NDA

Gt Hq · UK - Hybrid · Hybrid

• Employment typeFull-time
• Experience levelSenior
• Salary signalSalary not listed
• Last checked2 hours ago
• Source valid throughNov 5, 2026

Role Overview

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 Find Jobs in United Kingdom on Arbeitnow

Why It Matches

This full-time role is listed as hybrid in UK - Hybrid. The source does not expose a salary range, so confirm compensation before investing in a long application. The match score reflects role clarity, work mode, location, freshness, salary visibility, and description depth—not a promise that the employer will select a candidate.

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