At a Glance
- Tasks: Develop machine learning models for energy forecasting and support trading decisions.
- Company: Marlin Selection, a dynamic recruitment firm in London.
- Benefits: Competitive salary, collaborative environment, and opportunities for professional growth.
- Other info: Work closely with traders and analysts in a fast-paced environment.
- Why this job: Join a cutting-edge team and make an impact in European power markets.
- Qualifications: Experience in data science and machine learning, with strong analytical skills.
The predicted salary is between 60000 - 75000 £ per year.
Marlin Selection Recruitment in London is seeking a Power Analyst – Data Scientist to develop machine learning forecasting models that support trading decisions across European power markets. You will work closely with traders, analysts and data teams to improve forecasting accuracy and contribute to trading performance.
Your responsibilities include:
- Productionising time-series forecasting models
- Building forecasting pipelines
- Collaborating with data engineers to onboard new data feeds
Power Data Scientist: Energy Forecasting & Trading ML employer: Marlin Selection Recruitment
Join a dynamic and innovative Commodity Trading firm in London, where you will be part of a collaborative team dedicated to excellence in trade operations. The company offers a supportive work culture that prioritises employee growth through continuous learning opportunities and mentorship, ensuring you can advance your career while making a meaningful impact in the energy sector. With competitive benefits and a focus on work-life balance, this is an excellent opportunity for those looking to thrive in a fast-paced environment.
Contact Details:
Marlin Selection Recruitment Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Power Data Scientist: Energy Forecasting & Trading ML
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We think you need these skills to ace Power Data Scientist: Energy Forecasting & Trading ML
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!
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Craft a Tailored Cover Letter:For a full-time role at Marlin Selection Recruitment, 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 Marlin Selection Recruitment. 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 Marlin Selection Recruitment
✨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!
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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
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✨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.
