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

1. GT interview with Recruiter

2. Technical interview

3. Final interview