Lead machine learning engineer - Waterloo, City of Westminster

Location: Hybrid - London (The Strand) | 1 day per week in the office (flexibility provided)

Salary Banding: Competitive

Join Our Team as a Lead Machine Learning Engineer!

At our organisation, we are dedicated to connecting people to the energy they use safely, reliably, and efficiently. We're looking for a dynamic Lead Machine Learning Engineer to join our global Data Science team based in Waterloo, City of Westminster. This is your chance to shape the future of energy and technology!

About the Role: As a Lead Machine Learning Engineer, you will play a pivotal role in bringing data science models into production, ensuring they are reliable, scalable, and automated. You will work in a collaborative environment with teams across the US and the UK, focusing on innovative solutions that impact millions.

This role sits within a global Data Science team, consisting of four teams (three in the US, one in the UK). The focus of this role is on bringing data science models into production, ensuring reliability, scalability, and automation.

What You'll Do:

Lead end-to-end machine learning projects, translating concepts into production-ready solutions.

Develop robust data pipelines to operationalize models.

Fix production bugs and maintain smooth operations.

Design and provision Azure cloud infrastructure with an emphasis on automation.

Support initiatives that collect and analyse weather data to predict power outages due to storms.

Build platform tooling, including internal libraries and CLI tools.

Enhance CI/CD pipelines and model monitoring frameworks.

Collaborate with IT teams to improve project delivery.

Mentor and train junior engineers, sharing your expertise and passion!

Must-have:
. Strong Python expertise (experience with Pandas, scikit-learn).
. High proficiency in SQL.
. Software Engineering & DevOps experience: Terraform, GitHub Actions, Packer, Airflow.
. Cloud & Infrastructure experience: Azure (VMs, Web Apps, Managed Databases).
. Containerization & orchestration: Docker, Kubernetes.
. Linux/Windows VM administration & Scripting (Bash/PowerShell).
. Understanding of security and networking principles.

Interview Process:

1. Technical Interview (1 hour) - Dive deep into your technical expertise.

2. Take-home assessment - Showcase your ability to prepare production-ready code.

3. Final Interview (2 hours) - Discuss your assessment, problem-solving approach, and cultural fit.

If you're a passionate and experienced machine learning engineer ready to take the lead and make a difference, we want to hear from you! Join us in our mission to connect people with the energy they need-apply today!