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 an AI/ML Engineer to join a UK-based client in the healthcare and pharmacy domain.
The role focuses on forecasting and time-series modeling, developing solutions that directly improve operational efficiency.

Project duration: 12 weeks (with possible extension).
Start date: June 15 (flexible - part-time start possible).

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

Essential knowledge, skills & experience (must-have):
4+ years of experience in Machine Learning / Data Science

Proven experience with forecasting / time-series modeling (Prophet, ARIMA/SARIMA, LSTM, TimeGPT, XGBoost or similar)

Strong Python skills (Pandas, NumPy, scikit-learn, PyTorch)

Experience with model deployment and production ML systems

Familiarity with data preprocessing and feature engineering for time-series data

Familiarity with cloud environments (Azure, AWS, or GCP)

Version control (Git) and SQL

Advanced English level

Nice-to-have:
Experience with Generative AI / LLMs

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

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

Final interview