Low Carbon Contracts Company
Quantitative Development Intern
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Application Deadline: 18 October 2026
Department: Analytics
Employment Type: Internship
Location: Leeds, England, United Kingdom
Compensation: £30,000 / year
Description
Contract type: Fixed-term contract, flexible contract available: 6-12 months
This internship is aimed at students still undergoing their undergraduate/masters degree.
Hours: 37.5/week
Salary: £30,000
Location: Leeds (LS1 4HR)
WFH policy: Employees are required to attend the office 2 days/week
Flexible working: Variety of flexible work patterns subject to line manager discretion e.g. Compressed 9-day fortnight.
Reports to: Quantitative Development Manager
Deadline Note: We reserve the right to close the advert before the advertised deadline if there are a high volume of applications.
Role Summary
LCCC internships in the Analytics team provide an opportunity for successful candidates to contribute to some of the UK’s most exciting and high-profile Net Zero programmes and projects. These roles also offer the chance to support innovative low-carbon schemes driving progress toward the UK’s 2050 Net Zero target.
During the 6-12 month internship, the Quant Dev Intern will have the opportunity to work on the development of both scheme forecasting models and analytical models, as well as the publication of supporting technical documentation. This quantitative development role requires a deep skillset within computing (python, spark) and statistical modelling; providing technical leadership on the development, testing and debugging of the code underpinning our most business critical forecasting models.
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The ideal candidate will combine an understanding of energy market fundamentals with state-of-the-art optimisations and data science techniques. They will be required to take on complex challenges with a sense of urgency and enthusiasm, developing and communicating data insights in a clear and succinct way. Furthermore the candidate will be adaptable and curious, with a strong willingness to learn and develop.
Key Responsibilities
- Explore and clean datasets required for modelling purposes
- Design and build short and long-term energy models used in forecasting, analysis and for generating insights that support various strategic initiatives
- Testing and deploying updates to the models
- Producing high quality technical documentation
- Identifying and streamlining inefficient processes
- Supporting other teams with modelling, analysis and automation
Skills, Knowledge, and Expertise
Essential
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An expected 2:1 undergraduate degree in STEM subjects.
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The below experiences can be gained from your academic work or job experiences:
- Experience in modelling, forecasting and analysis of complex real life systems
- The ability to convey complex technical concepts to those with little or no modelling background in a meaningful, relevant and engaging matter
- Experience with Python and Object Oriented Programming
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Desirable
- Understanding of the energy markets
- Working knowledge of machine learning tools and statistical techniques
- Experience with Git and cloud computing
Employee Benefits
Benefits
As if contributing to and supporting work that makes life better for millions wasn’t rewarding enough, we offer a full range of benefits too. Key benefits that may be available depending on the role include:
- Annual performance based bonus, up to 10%
- 25 days annual leave, plus eight bank holidays
- Up to 8% pension contribution
- Financial support and time off for study relevant to your role, plus a professional membership subscription
- Employee referral scheme (up to £1500), and colleague recognition scheme
- Family friendly policies, including enhanced maternity leave and shared parental leave
- Free, confidential employee assistance, including financial management, family care, mental health, and on-call GP service
- Three paid volunteering days a year
- Season ticket loan and cycle to work schemes
- Family savings on days out and English Heritage, gym discounts, cash back and discounts at selected retailers
- Employee resource groups
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