At a Glance
- Tasks: Develop innovative machine learning models to tackle challenging green-field problems.
- Company: Join an IoT innovator passionate about sustainable technology and improving lives.
- Benefits: Generous annual leave, excellent benefits, and a salary sacrifice car scheme.
- Other info: Hybrid work model based in London with great career growth opportunities.
- Why this job: Make a real impact with cutting-edge tech in a collaborative and agile team.
- Qualifications: MSc/MSci/PhD in a quantitative field and strong Python skills required.
The predicted salary is between 63000 - 77000 £ per year.
Would you thrive in a fast-scaling business, solving novel problems in collaborative teams? Are you interested in developing machine learning models for difficult green-field problems? If so, you could be the person we are searching for. We are an IoT innovator working to scale our product deployments across the UK and EU. We are passionate about developing technology that will change paradigms, contribute to a sustainable future, and improve lives. We are building Virtual Energy Infrastructure using our world-leading machine learning algorithms.
We’re looking for a Machine Learning Research Engineer to work with us in our Data Science and AI Team. In this team, we build custom algorithms that use novel approaches to solve our business needs. This isn’t a prompt engineering or LLM role - finding a solution to our problems requires a deep understanding of Machine Learning, software development, and the problems themselves. You will be working with large, complex, and unique datasets to solve a wide range of difficult statistical, mathematical, and physical engineering problems.
To achieve this, you will work with cutting-edge technologies in a highly collaborative environment. Key to this role is the ability to envision and design new algorithm products while carefully considering the practicality of rollout, wider strategic implications, and any legal or ethical considerations – and then taking these products from conception to deployment. This will require strong software engineering expertise and excellent machine learning proficiency. The ideal candidate brings not just technical skills, but an intellectual curiosity and eagerness to expand their knowledge across diverse technical domains.
You will be working with an enthusiastic, agile and highly skilled team to deliver a paradigm-changing technology across Europe with a positive environmental and social impact. Our world-leading algorithmic products are at the core of our business, so as a part of the Data Science and AI Team, you will have a high level of exposure to the wider business. This role is based in our London office at Liverpool Street (hybrid in-office and work-from-home).
Experience Level: Hiring at a range of experience levels; 0-5 years of experience
We are looking for:
- MSc/MSci/PhD in a highly quantitative field (Mathematics, Computer Science, Physics, etc)
- Strong knowledge of Python and appropriate Machine Learning libraries and frameworks
- Ability to work autonomously to conduct research and pose difficult questions in order to build scalable algorithmic solutions to hard problems from the ground up
- Strong Machine Learning and Statistics knowledge; both in classical models and neural-network-style models
- Strong Software Engineering fundamentals
- Enthusiasm to learn and contribute to a culture of learning
- Advantageous - PhD (in a highly quantitative field)
What we offer: Generous annual leave allowance, excellent benefits package including salary sacrifice car scheme
Machine Learning Research Engineer in London employer: Knauf Energy Solutions
Knauf Energy Solutions is an exceptional employer that champions opportunity and inclusivity, making it an ideal place for aspiring professionals to thrive. With a strong focus on employee growth, we offer a range of benefits including enhanced holiday pay, hybrid working options, and comprehensive health plans, all within a supportive and values-driven culture. Join our global team and take advantage of unique career progression routes while contributing to a purpose that aims to create a better tomorrow for everyone.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Research Engineer in London
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We think you need these skills to ace Machine Learning Research Engineer in London
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 Knauf Energy Solutions, 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 Knauf Energy Solutions. 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 Knauf Energy Solutions
✨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 Knauf Energy Solutions!
✨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.
