Energy Data Scientist β€” Production ML in London

Energy Data Scientist β€” Production ML in London

London Full-Time 50000 - 70000 Β£ / year (est.) Hybrid
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At a Glance

  • Tasks: Own projects end-to-end, deploying ML models to enhance energy efficiency.
  • Company: Innovative energy startup in Central London tackling real-world challenges.
  • Benefits: Flexible work schedule, direct client interaction, and visa sponsorship available.
  • Other info: Dynamic environment with opportunities for growth and innovation.
  • Why this job: Make a difference in energy systems while developing your ML skills.
  • Qualifications: Strong ML skills, production-ready Python coding, and curiosity about energy.

The predicted salary is between 50000 - 70000 Β£ per year.

An innovative energy startup in Central London is seeking multiple Data Scientists to help address energy challenges. You will own projects end-to-end, deploying machine learning models that improve energy usage efficiency.

Candidates should have strong ML skills, write production-ready Python, and be curious about energy systems.

The role offers flexibility with 2-3 office days per week and provides opportunities for direct client interaction. Visa sponsorship is available if needed.

Energy Data Scientist β€” Production ML in London employer: Wave Group

At Wave Talent, we pride ourselves on being an exceptional employer, offering a unique opportunity for the Founding BDR to shape the future of our fast-growing fintech. With a vibrant work culture that encourages innovation and direct access to leadership, employees benefit from a range of perks including a premium membership to our investor collective and a gym membership. Our commitment to employee growth is evident as you will be at the forefront of building our outbound engine, allowing you to develop your skills in a dynamic environment while contributing to our impressive year-on-year growth.

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Contact Details:

Wave Group Recruitment Team

StudySmarter Expert Advice🀫

We think this is how you could land Energy Data Scientist β€” Production ML in London

✨Get Involved in Data Science Meetups

Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Wave Group!

✨Show Off Your Projects

Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Energy Data Scientist β€” Production ML in London at Wave Group.

✨Leverage Professional Networks

Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Wave Group.

✨Apply Directly through Our Website

When you find a suitable opening like Energy Data Scientist β€” Production ML in London at Wave Group, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!

We think you need these skills to ace Energy Data Scientist β€” Production ML in London

Machine Learning
Python
Energy Systems Knowledge
Project Ownership
Data Science
Production-Ready Code
Client Interaction

Some tips for your application 🫑

Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!

Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!

Craft a Tailored Cover Letter:For a full-time role at Wave Group, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.

Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Wave Group. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!

How to prepare for a job interview at Wave Group

✨Brush Up on Your Statistics

For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!

✨Showcase Your Projects

Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!

✨Get Comfortable with Python and R

Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Wave Group!

✨Prepare for Case Studies

Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.