Data Science Intern

We are Lightsource bp – and we’re on a mission to become a global leader in onshore renewables, anchored by our proven track record in solar development. For over a decade we’ve been actively working to diversify the way our world is powered with sustainable and responsible renewable power. We work to safely deliver affordable, reliable, large-scale onshore renewable and energy storage solutions to help the world decarbonise. Our growing business is constantly innovating and investing to help drive the energy transition. Our people and projects are focused on supporting long‑term sustainable growth and energy security. Lightsource bp operates with five core values: Safety, Integrity, Respect, Sustainability, and Drive.

As a Data Science Intern at Lightsource bp, you will work on high‑impact optimisation and decision‑science problems that underpin our energy storage and grid‑integration strategy. You’ll contribute to research, design, experiment, implement and test approaches (modelling, simulation, reinforcement learning and optimization) that support improved operational decision‑making for battery energy storage systems (BESS). This internship is designed for a PhD‑level researcher who wants to apply cutting‑edge methods to real‑world energy systems within a global renewable energy organisation.

The Details

• Duration: 1 June – 21 August 2026 (12 weeks)
• Location: London or Milan (Hybrid Working – 3 days per week in office)
• Type: Full‑time internship
• Education Required: Currently pursuing a PhD (or final‑year PhD candidate) in a quantitative field; Master's degree required as minimum. We also consider alternative educational paths if you can demonstrate equivalent experience in RL, optimisation, or advanced machine learning.

Responsibilities of the Role:

• Develop and evaluate reinforcement learning (RL), (constrained) optimisation, or simulation models to support BESS dispatch strategy.
• Prototype algorithms for sequential decision‑making under uncertainty, incorporating market, operational, and physical system constraints.
• Analyse large time‑series and operational datasets to extract actionable insights that support energy trading, grid services, and storage optimisation.
• Collaborate closely with data scientists and product teams to design experiments and validate models.
• Communicate findings clearly to both technical and non‑technical stakeholders through documentation, visualisations, and presentations.
• Contribute to reproducible, high‑quality research code and experimentation pipelines.
• Stay current with emerging methods in RL, optimisation, and energy systems research.

Experience & Knowledge Required:

• Experience conducting research or applied work in one or more of: reinforcement learning, control systems, (constrained) optimisation, probabilistic modelling or simulation.
• Strong understanding of sequential decision‑making frameworks (e.g., MDPs, policy optimisation, planning).
• Experience working with large datasets, experimental design, and empirical model evaluation.
• Understanding of numerical methods, statistical learning, or stochastic processes.
• Familiarity with energy systems, grid flexibility markets, or storage optimisation is a plus but not mandatory.

Skills Required:

• Strong proficiency in Python and its scientific computing toolkit (JAX, Pyomo, NumPy, Scipy, Statsmodels, Pandas/Polars and similar).
• Solid knowledge of PyTorch, Gymnasium and one or more of the following reinforcement learning frameworks: StableBasleine3, TorchRL, RLlib, SKRL.
• Ability to design and evaluate RL agents or optimisation algorithms.
• Strong analytical and mathematical reasoning skills.
• Ability to communicate complex technical findings clearly and concisely.
• Ability to work within a production‑grade codebase.
• Experience with scientific coding best practices (version control, experimentation tracking, reproducibility).
• Ability to work in a research environment while following agile ways of working (i.e. continuous iterative delivery and improvement).
• Curiosity, autonomy and good organizational and time management skills.

Education Required:

• Currently pursuing a PhD (or in the final years of a PhD) in Machine Learning, Computer Science, Applied Mathematics, Engineering, Operations Research, or a related quantitative field. We consider other educational paths provided the candidate can demonstrate extensive experience in one or more of the areas mentioned before.

Why you’ll want to work for us:

• Our company is a place where you can be yourself and grow, a place where your ideas and opinions matter.
• We pride ourselves on being an inclusive community, where every individual is valued and treated with respect.
• Our culture is driven by our core values – from operating safely to ensuring our projects are responsible.
• Alongside a competitive salary, we offer a variety of benefits including annual bonus, retention bank, health insurance, pension and other local benefits.
• Beyond your day‑to‑day working life at Lightsource bp, there’s a variety of initiatives that will contribute to your own personal development.

Our core values: Lightsource bp truly cares about creating a sustainable future through safe, responsible and meaningful low carbon energy projects. Our core values of Safety, Integrity, Respect, Sustainability and Drive are the guiding principles for everything we do.