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For Companies At a Glance
- Tasks: Lead ML projects, develop data pipelines, and mentor junior team members.
- Company: Join National Grid, a leader in energy transition and connectivity.
- Benefits: Enjoy a competitive salary, bonuses, flexible benefits, and a supportive work culture.
- Why this job: Make an impact in the energy sector while working with cutting-edge technology and diverse teams.
- Qualifications: Experience in ML Engineering, Python, SQL, and cloud infrastructure is essential.
- Other info: This is a hybrid role based in London with opportunities for growth and development.
The predicted salary is between 80000 - 95000 £ per year.
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.
As a Lead Machine Learning Engineer on the National Grid Data Science team, you will develop data pipelines, take data science prototype models to production, fix production bugs, monitor operations, and provision the necessary infrastructure in Azure.
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
- 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.
- 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.
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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, such as Azure, Python libraries like Pandas and scikit-learn, and tools like Docker and Kubernetes. Having hands-on experience with these will give you a significant edge during interviews.
✨Tip Number 2
Showcase your leadership skills by discussing any previous experiences where you led projects or mentored junior team members. This aligns perfectly with the role's requirement to lead machine learning projects and train others.
✨Tip Number 3
Prepare to discuss your experience with data pipelines and model deployment. Be ready to provide examples of how you've taken prototypes to production and improved operational processes, as these are key responsibilities for the position.
✨Tip Number 4
Research National Grid's initiatives in the energy transition and be prepared to discuss how your skills can contribute to their mission. Showing that you understand their goals and values will help you stand out as a candidate who is genuinely interested in the role.
We think you need these skills to ace Lead Machine Learning Engineer
Some tips for your application 🫡
Understand the Role: Make sure to thoroughly read the job description for the Lead Machine Learning Engineer position. Understand the key responsibilities and required skills, especially in Python, Azure, and data science.
Tailor Your CV: Customize your CV to highlight relevant experience in machine learning, software engineering, and cloud infrastructure. Emphasize your hands-on experience with the technologies mentioned, such as Python, SQL, and Azure.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for the energy sector and how your skills align with National Grid's mission. Mention specific projects or experiences that demonstrate your ability to lead machine learning initiatives.
Highlight Team Collaboration: Since the role involves mentoring and working with data scientists and IT teams, be sure to include examples of your teamwork and leadership skills. Show how you can contribute to a collaborative environment.
How to prepare for a job interview at National Grid
✨Showcase Your Technical Skills
Be prepared to discuss your hands-on experience with Python, Azure, and other relevant technologies. Highlight specific projects where you've developed data pipelines or taken machine learning models to production.
✨Demonstrate Leadership Experience
As a Lead Machine Learning Engineer, you'll be expected to mentor junior team members. Share examples of how you've led projects or teams in the past, focusing on your ability to guide and support others.
✨Understand the Company’s Mission
Familiarize yourself with National Grid's role in the energy transition and their commitment to inclusivity. Be ready to discuss how your values align with theirs and how you can contribute to their mission.
✨Prepare for Behavioral Questions
Expect questions that assess your problem-solving skills and teamwork. Use the STAR method (Situation, Task, Action, Result) to structure your responses, particularly when discussing challenges you've faced in previous roles.
