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For Companies At a Glance
- Tasks: Lead end-to-end Machine Learning projects and develop data pipelines.
- Company: Join National Grid, a leader in energy transition and connectivity.
- Benefits: Enjoy a competitive salary, bonuses, and flexible benefits like share incentives.
- Why this job: Make an impact in energy while working with cutting-edge tech and mentoring others.
- Qualifications: 7+ years in Software Engineering, ML Engineering, or Data Science; expertise in Python and SQL required.
- Other info: Work with a collaborative team and advance your career in a supportive environment.
The predicted salary is between 48000 - 84000 £ per year.
Lead Machine Learning Engineer
Date: Jan 9, 2025
Location: London, GB, WC2N 5EH
Company: National Grid
About The Role
At National Grid, we keep people connected and society moving. But it’s so much more than that. National Grid supplies us with the environment to make it happen. As we generate momentum in the energy transition for all, we don’t plan on leaving any of our customers in the dark. So, join us as a Lead Machine Learning Engineer, and find your superpower.
National Grid is hiring a Lead Machine Learning Engineer for our IT & Digital department. This is a hybrid role based in London.
Key Accountabilities
- Lead Machine Learning projects end-to-end.
- Develop platform tooling (e.g., internal conda library, CLI tool for project setup, and provisioning infrastructure) for the Data Science team.
- Work with data scientists to understand their data needs and put together data pipelines to ingest data.
- Work with data scientists to take data science model prototypes to production.
- Mentor and train junior team members.
- Work with internal IT teams (security, Cloud, Global Active Directory, Architecture, Networking, etc.) to advance the team’s projects.
- Enhance code deployment lifecycle.
- Improve model monitoring frameworks.
- Refine project operations documentation.
- Design, provision, and maintain the cloud infrastructure needed to support Data Engineering, Data Science, Machine Learning Engineers, and Machine Learning Operations.
- Write high-quality code that has high test coverage.
- Participate in code reviews to help improve code quality.
Technologies/Tools we use: Python, Azure (Virtual Machines, Azure Web Apps, Cloud Storage, Azure ML), Anaconda packages, Git, GitHub, GitHub Actions, Terraform, SQL, Artifactory, Airflow, Docker, Kubernetes, Linux/Windows VMs.
About You
At least 7 years of hands-on industry experience in some combination of Software Engineering, ML Engineering, Data Science, DevOps, and Cloud Infrastructure work.
Expertise in Python which includes experience in libraries such as Pandas, scikit-learn. High proficiency in SQL.
Knowledge of best practices in software engineering is necessary.
At least 5 years of hands-on industry experience in some combination of the following technologies: Python ecosystem, Azure (VMs, Web Apps, Managed Databases), GitHub Actions, Terraform, Packer, Airflow, Docker, Kubernetes, Linux/Windows VM administration, Shell scripting (primary Bash but PowerShell as well).
A solid understanding of modern security and networking principles and standards.
A foundational knowledge of Data Science is strongly preferred.
Bachelor’s or higher degree in Computer Science, Data Science, and/or related quantitative degree is preferred from an accredited institution.
More Information
A salary between £80,000 – £95,000 – dependent on capability.
As well as your base salary, you will receive a bonus of up to 15% of your salary for stretch performance and a competitive contributory pension scheme where we will double match your contribution to a maximum company contribution of 12%. You will also have access to a number of flexible benefits such as a share incentive plan, salary sacrifice car and technology schemes, support via employee assistance lines and matched charity giving to name a few.
At National Grid, we work towards the highest standards in everything we do, including how we support, value and develop our people. Our aim is to encourage and support employees to thrive and be the best they can be. We celebrate the difference people can bring into our organisation, and welcome and encourage applicants with diverse experiences and backgrounds, and offer flexible and tailored support, at home and in the office.
Our goal is to drive, develop and operate our business in a way that results in a more inclusive culture. All employment is decided on the basis of qualifications, the innovation from diverse teams & perspectives and business need. We are committed to building a workforce so we can represent the communities we serve and have a working environment in which each individual feels valued, respected, fairly treated, and able to reach their full potential. #J-18808-Ljbffr
Lead Machine Learning Engineer employer: National Grid
Contact Detail:
National Grid Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Machine Learning Engineer
✨Tip Number 1
Familiarize yourself with the specific technologies mentioned in the job description, especially Azure and Python libraries like Pandas and scikit-learn. Having hands-on experience with these tools will not only boost your confidence but also demonstrate your readiness for the role.
✨Tip Number 2
Showcase your ability to lead projects by discussing any previous experiences where you managed end-to-end machine learning projects. Highlight your problem-solving skills and how you mentored junior team members, as this aligns perfectly with the responsibilities of the position.
✨Tip Number 3
Network with current or former employees of National Grid on platforms like LinkedIn. Engaging with them can provide you with valuable insights about the company culture and expectations, which can be beneficial during interviews.
✨Tip Number 4
Prepare to discuss your understanding of modern security and networking principles, as well as your experience with cloud infrastructure. Being able to articulate your knowledge in these areas will set you apart from other candidates.
We think you need these skills to ace Lead Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in Machine Learning, Data Science, and Cloud Infrastructure. Emphasize your hands-on experience with Python, Azure, and relevant tools mentioned in the job description.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for the energy transition and how your skills align with the responsibilities of a Lead Machine Learning Engineer. Mention specific projects or experiences that demonstrate your ability to lead and mentor.
Showcase Relevant Projects: Include examples of past projects where you developed data pipelines, took models to production, or improved code deployment lifecycles. Highlight any experience with mentoring junior team members or collaborating with IT teams.
Proofread and Edit: Before submitting your application, carefully proofread your documents for any grammatical errors or typos. Ensure that your application is clear, concise, and professional to make a strong impression.
How to prepare for a job interview at National Grid
✨Showcase Your Technical Expertise
Be prepared to discuss your hands-on experience with Python, Azure, and the various tools mentioned in the job description. Highlight specific projects where you developed data pipelines or took machine learning models to production.
✨Demonstrate Leadership Skills
As a Lead Machine Learning Engineer, you'll be expected to mentor junior team members. Share examples of how you've successfully led projects or trained others in your previous roles.
✨Understand the Company’s Mission
Familiarize yourself with National Grid's goals in the energy transition. Be ready to discuss how your skills can contribute to keeping customers connected and advancing their projects.
✨Prepare for Behavioral Questions
Expect questions that assess your problem-solving abilities and teamwork. Use the STAR method (Situation, Task, Action, Result) to structure your responses and provide clear examples from your past experiences.
