At a Glance
- Tasks: Lead the creation of forecasting and optimisation models for energy asset trading.
- Company: GridBeyond is revolutionising energy markets for a sustainable, zero-carbon future.
- Benefits: Enjoy hybrid working options and a competitive salary package.
- Why this job: Make a real impact on global energy solutions while collaborating with innovative minds.
- Qualifications: Degree in a quantitative field; experience in UK/Ireland power markets required.
- Other info: Join a rapidly growing team dedicated to sustainability and cutting-edge technology.
The predicted salary is between 60000 - 84000 Β£ per year.
Who are GridBeyond? At GridBeyond, we envision a future where we work hand in hand with our customers to unearth the full potential of energy assets, crafting sustainability, resilience, and affordability to steer the course towards a zero-carbon future. Our technology unlocks the potential of every connected asset, from utility-scale renewable generation and battery storage to industrial loads. By intelligently dispatching flexibility into the right market at the right time, asset owners and energy consumers can unlock new revenues and savings, enhance resilience, manage price volatility, and support the transition to a net zero future. How does GridBeyond do this? We do this by living our Mission, leveraging AI to innovate and collaborate with our customers creating optimal value from renewable energy generation, demand and storage to deliver a zero-carbon future. Whatβs the business and culture like?Over the last 24 months, we have doubled the number of people that work with us and expanded our business operations over an international landscape. With people working in Ireland, UK, US, Japan and Australia-we are company that understands the importance of connection and thrive on the purpose of our work. We are proud to contribute to a greener, cleaner world for everyone to live in. Whatβs this role about?We are seeking a Senior Quantitative Data Scientist with extensive expertise in the UK and Irish electricity markets to lead the creation of forecasting and optimisation models that underpin asset trading and demand-side flexibility strategies. This position focuses on leveraging advanced quantitative techniques to guide operational decisions across day-ahead, intraday, balancing, and system services markets. The successful candidate will address challenges such as short-term price prediction, flexible asset optimisation, and revenue modeling for both front-of-the-meter (FTM) and behind-the-meter (BTM) assets, including batteries and demand response portfolios. This is not a generic analytics role; the ideal applicant will have a proven track record of developing applied models that accurately reflect the structure and dynamics of real power markets and flexibility products.What will I be doing?Develop and maintain short-term market forecasting models for day-ahead, intraday, and balancing prices.Create optimisation models that facilitate real-time scheduling and market participation of flexible assets, including:Battery storage (FTM and BTM)Industrial demand responseDSU and aggregated portfoliosDesign and implement co-optimisation models that operate across energy and ancillary services markets (e.g., DS3, Dynamic Containment, Fast Reserve).Assess and simulate the effects of forecast errors, price volatility, and baseline methodologies on trading performance and service delivery.Support the development of forecast-based bidding strategies, integrating model outputs into operational decision-making tools.Collaborate closely with traders, control engineers, and commercial teams to ensure models are aligned with real operational and market constraints.Develop commercial and forecasting models to aid renewable energy trading, including structuring and valuing wind and solar PPAs under merchant and route-to-market frameworks.Contribute to a shared model development framework that balances rigor, interpretability, and deployability.What do I need to be a good technical fit for this role? Degree in a quantitative discipline such as mathematics, statistics, engineering, or physics (postgraduate qualification preferred).Proficiency in Python (preferred) or R, with experience in libraries like pandas, scikit-learn, statsmodels, Pyomo, or similar tools.Strong expertise in time series forecasting, stochastic modelling, and optimisation under uncertainty.Experience with linear or mixed-integer optimisation methods, ideally within an energy market context.Knowledge of probabilistic forecasting, scenario generation, and model back testing techniques.SQL and data handling skills; familiarity with cloud platforms (Azure, AWS) is advantageous.Domain Knowledge:At least 3β5 years of experience working within UK and/or Irish power markets. Deep understanding of price formation, imbalance risk, and trading structures in day-ahead, intraday, and system services markets.Experience modelling the operation and dispatch of flexible energy assets, such as BTM and FTM batteries or demand response portfolios.Knowledge of baseline methodologies and market rules for demand response participation in Ireland (DS3) and Great Britain (e.g., FFR, DC, BM).Experience working with wind and solar assets in wholesale trading, including PPA valuation, hedging strategies, imbalance exposure modelling, and intraday forecasting for merchant portfolios.Desirable Skills:Experience with co-optimising assets across multiple services (e.g., energy and frequency response).Familiarity with demand-side asset control logic, aggregator models, or control platform integration.Prior involvement in algorithmic bidding, revenue stacking, or developing trading signals.If you are a seasoned quantitative modeller with practical experience in energy markets and a keen interest in how flexibility and renewables can be accurately priced, forecasted, and optimised, we welcome your application.What behaviors are rewarded at GridBeyond?Customer Obsession- We keep our customers at the forefront of every decision we make.Relentless- We push beyond boundaries, overcome obstacles. We refuse to give up and will never settle for less than world class.Integrity β We are committed to conducting ourselves with honesty, ethics, and transparency. We foster trust and respect with our colleagues, customers and partners.Agility- We value agility and the willingness to embrace new ideas and technologies.Collaboration- We foster a culture of cooperation, communication, and shared knowledge.What do we offer:A senior technical position within a team leading in flexibility trading and asset optimisation.The chance to influence how flexible assets are valued and scheduled across energy and ancillary markets.Exposure to real operational constraints and commercial strategy, beyond offline modelling. Hybrid working options and a competitive salary package commensurate with experience.This role offers an exciting opportunity to influence the global energy landscape through strategic financial leadership at a pioneering company.Join us to be part of a forward-thinking team dedicated to innovative energy solutions and a sustainable future. Apply now to contribute to cutting-edge projects in global energy markets and smart grid technologies.
Senior Quantitative Data Scientist - Power Markets Optimisation employer: LinkedIn
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LinkedIn Recruiting Team
StudySmarter Expert Advice π€«
We think this is how you could land Senior Quantitative Data Scientist - Power Markets Optimisation
β¨Tip Number 1
Familiarise yourself with the latest trends and developments in the UK and Irish electricity markets. Understanding current market dynamics will not only help you during interviews but also demonstrate your genuine interest in the role.
β¨Tip Number 2
Network with professionals in the energy sector, especially those involved in quantitative modelling and trading. Engaging with industry experts can provide valuable insights and potentially lead to referrals for the position.
β¨Tip Number 3
Prepare to discuss specific projects or models you've worked on that relate to forecasting and optimisation in energy markets. Being able to articulate your hands-on experience will set you apart from other candidates.
β¨Tip Number 4
Stay updated on the latest AI technologies and tools relevant to energy asset optimisation. Showing that you are proactive about learning and adapting to new technologies can make a strong impression on the hiring team.
We think you need these skills to ace Senior Quantitative Data Scientist - Power Markets Optimisation
Some tips for your application π«‘
Tailor Your CV: Make sure your CV highlights relevant experience in quantitative modelling, energy markets, and any specific projects that align with the role at GridBeyond. Use keywords from the job description to demonstrate your fit.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for renewable energy and your understanding of the UK and Irish power markets. Mention specific experiences that relate to the responsibilities outlined in the job description.
Showcase Technical Skills: In your application, emphasise your proficiency in Python or R, and detail your experience with libraries like pandas and scikit-learn. Provide examples of how you've applied these skills in real-world scenarios.
Demonstrate Industry Knowledge: Highlight your understanding of price formation, trading structures, and demand response methodologies in your application. This will show that you are not only technically skilled but also knowledgeable about the industry.
How to prepare for a job interview at LinkedIn
β¨Showcase Your Technical Skills
Make sure to highlight your proficiency in Python or R, especially with libraries relevant to the role like pandas and scikit-learn. Be prepared to discuss specific projects where you've applied these skills, particularly in the context of energy markets.
β¨Demonstrate Market Knowledge
Familiarise yourself with the UK and Irish electricity markets, including price formation and trading structures. Be ready to discuss your experience with flexible energy assets and how youβve modelled their operation in real-world scenarios.
β¨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in forecasting and optimisation. Think about past challenges you've faced in modelling and how you overcame them, particularly regarding price volatility and forecast errors.
β¨Emphasise Collaboration and Communication
GridBeyond values teamwork, so be prepared to discuss how you've collaborated with traders, engineers, and commercial teams in previous roles. Highlight any experiences where your communication skills helped align technical models with operational needs.