Data Scientist (Energy)

TL;DR

Data Scientist (Energy): Developing and evolving algorithmic trading strategies for grid-scale battery assets with an accent on data analysis, optimization logic, and machine learning. Focus on translating complex market insights into production-ready decision-making frameworks to improve trading performance and value capture.

Location: Must be based in or able to commute to London (Hybrid)

Company

hirify.global is a fast-paced, inclusive energy technology company dedicated to building a sustainable future by enabling a zero-carbon grid through innovative storage solutions.

What you will do

• Analyze market, trading, and operational data to identify opportunities for performance improvement.
• Design, develop, and evaluate machine learning and optimization-based solutions for trading decisions.
• Develop and test new trading strategies using historical analysis and simulation techniques.
• Contribute to the evolution of optimiser objectives, constraints, and decision-making frameworks.
• Collaborate with engineers and traders to implement and evaluate data-driven ideas in production.
• Build tools and workflows to improve visibility into system performance and reduce manual processes.

Requirements

• Degree in Computer Science or a related discipline, or equivalent workplace experience.
• 2+ years of relevant experience in data science, quantitative analysis, or research.
• Proficiency in writing clean, maintainable, and production-ready Python code.
• Experience developing, evaluating, and deploying machine learning models.
• Solid knowledge of applied statistics, probability, and experimental best practices.
• Familiarity with optimization techniques applied to real-world decision-making.

Culture & Benefits

• 25 days of annual leave plus public holidays and festive office closure.
• Flexible working approach with a hybrid office presence.
• Enhanced parental leave: 6 months full salary for primary caregiver, 1 month for secondary.
• Premium Private Medical Insurance with pre-existing conditions covered from day one.
• Annual salary review and personal learning and development budget.
• Support for professional membership fees and subscriptions.

Hiring process

• First stage interview with hiring manager.
• Take-home technical test.
• Second stage with Senior Data Team.
• Final stage with hiring manager and Head of Trade Products.