ML Engineer (Forecasting) | GT

GT was founded in 2019 by a former Apple Nest and Google executive. GTs mission is to connect the worlds 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

Were 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: 4 months (with possible extension).

Start date: early November (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

1. GT interview with Recruiter

2. Technical interview

3. Final interview