Data Science and Software Engineer Manager / Tech Lead

Data analytics in the energy and climate space is growing at an incredible rate. Blunomy provides data-based solutions to support the energy industry, financial institutions and supply chains through their digitisation, decentralisation and decarbonisation transitions. Our tools support improved energy market operability, automation and planning. We are also developing digital solutions that better measure impact and transition speed, to help decarbonise financial resources.

Responsibilities

• Managing the end‑to‑end delivery of the project
• Engaging and communicating with the client
• Anticipating and managing project risks, timelines, and budget
• Code quality (PR review)
• Managing and leading analysts and senior analysts
• Act as a Product Owner for data products, managing the product backlog , prioritizing features based on ROI, and defining clear User Stories
• Drive the strategic roadmap for Agentic AI , leveraging LLMs to create autonomous decision‑support tools for decarbonisation and financial transition

In this setting, you will be exposed to the diversity of current energy challenges, leveraging your data and analytical skills to address them. Depending on the objectives, projects will entail tasks of varying nature. Your first focus will be on projects requiring data analytics capabilities.

Qualifications

• Undergraduate degree or a postgraduate in a relevant discipline such as Computing Science, Statistics, Mathematics or Engineering
• 5+ years’ experience on data science projects and at least 2 years’ experience in managing projects and staff
• Background in data science or software development
• Experience or passion for energy, environment and/or sustainable development
• Knowledge of statistical and machine learning concepts and algorithms
• Ensure the scientific rigour of all deliverables by enforcing best practices in statistical validation and stochastic modeling for risk assessment
• Expertise in overseeing the development of models that balance algorithmic complexity with computational efficiency, ensuring scalability for large‑scale energy networks
• Experience with the data science toolkit: Python or R
• Source control (such as Git)
• Software engineering and app development skills
• Knowledge of cloud computing (Azure, GCP, AWS)