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- Tasks: Join a team to test and improve optimisation algorithms for energy systems.
- Company: Work with a global leader in energy solutions, driving innovation in cloud-native IT platforms.
- Benefits: Enjoy flexible working options and the chance to collaborate with industry experts.
- Why this job: Make a real impact on energy systems while developing your skills in a dynamic environment.
- Qualifications: Degree in Engineering, Mathematics, Physics or Operations Research; strong Python coding skills required.
- Other info: Ideal for those passionate about optimisation and energy markets.
The predicted salary is between 48000 - 72000 £ per year.
Our client, a global organisation is looking for an Optimisation SME in their Balancing Transformation programme. You will be responsible for delivering quality algorithm testing to production new tools to help balance the system. You will be working in a team of experts, engaging with the IT and the business in an Agile delivery environment, to deploy solutions in a state-of-the-art cloud-native IT platform.
Specific responsibilities include (but are not limited to):
- Work with the project team and the customer team on the Dispatch algorithm as part of Balancing Transformation programs.
- Test control and optimisation algorithms.
- Improve and maintain existing algorithms and control processes.
- Collaborate with internal teams for the implementation of new capabilities in our IT platform.
- Support in Quality Assurance of Modern Optimisation Algorithm.
- Coordinate with different functional teams and Business.
We are looking for someone who:
- Has an operations research background – excellent understanding of optimisation methods and control algorithms, and experience in applying them to real-world problems.
- Has solid coding skills in Python.
- Has an analytical mindset, attention to detail, and is comfortable with collecting and interpreting data to support end-to-end solution design.
- Has a keen interest in energy systems.
- Experience solving the optimisation problem using various techniques.
- Develop custom data models and algorithms to apply to data sets.
- Strong understanding of mathematical programming solvers available for your LP, QP and MIP (MILP, MIQP, and MIQCP) problems.
- Understanding of data collection, preprocessing and analysis.
- Familiarity with Python and Gurobi Optimizer.
- Has a degree in Engineering, Mathematics, Physics or Operations Research.
- Understanding of electricity markets and power system operations (including related economic dispatch/unit-commitment type of problems).
- Experience with delivering IT solutions in cloud-native environments.
- MSc or PhD in a related subject.
If you are interested and have the relevant experience, please apply promptly and we will contact you to discuss further.
Senior Optimization Engineer employer: Queen Square Recruitment
Contact Detail:
Queen Square Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Optimization Engineer
✨Tip Number 1
Familiarise yourself with the latest trends in optimisation methods and control algorithms. Being able to discuss recent advancements or case studies during your interview can demonstrate your passion and expertise in the field.
✨Tip Number 2
Brush up on your Python coding skills, especially in relation to data modelling and algorithm development. Consider working on personal projects or contributing to open-source projects that showcase your ability to apply these skills practically.
✨Tip Number 3
Network with professionals in the energy systems sector. Attend industry conferences or webinars where you can meet potential colleagues and learn more about the challenges they face, which could give you an edge in interviews.
✨Tip Number 4
Prepare to discuss your experience with cloud-native IT solutions. Be ready to share specific examples of how you've contributed to similar projects, as this will highlight your relevant experience and understanding of the role's requirements.
We think you need these skills to ace Senior Optimization Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in operations research, optimisation methods, and control algorithms. Include specific examples of how you've applied these skills to real-world problems, especially in energy systems.
Craft a Strong Cover Letter: In your cover letter, express your keen interest in the role and the company. Discuss your coding skills in Python and any relevant projects you've worked on, particularly those involving cloud-native IT solutions.
Showcase Relevant Skills: Emphasise your analytical mindset and attention to detail. Mention your familiarity with mathematical programming solvers like Gurobi Optimizer and your experience with data collection and analysis.
Highlight Educational Background: Clearly state your degree in Engineering, Mathematics, Physics, or Operations Research. If you have an MSc or PhD, make sure to mention it prominently, as it aligns with the qualifications they are looking for.
How to prepare for a job interview at Queen Square Recruitment
✨Showcase Your Technical Skills
Be prepared to discuss your coding skills in Python and your experience with optimisation methods. Bring examples of algorithms you've developed or improved, and be ready to explain the thought process behind them.
✨Demonstrate Your Analytical Mindset
Highlight your ability to collect and interpret data. Prepare to discuss how you've used data to support solution design in previous roles, especially in relation to energy systems and optimisation problems.
✨Familiarise Yourself with the Company’s Projects
Research the specific Balancing Transformation programme and understand its goals. Being knowledgeable about the company's projects will show your genuine interest and help you align your answers with their objectives.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. Think of examples where you've successfully tackled optimisation challenges, particularly in cloud-native environments or energy systems.
