Data Scientist

Data Scientist

Habitat is a fast growing technology company focussed on the physical and financial optimisation of energy storage and renewable generation assets globally through complex models and trading. By maximising the returns from these assets we aim to drive investment in renewable energy and accelerate the transition to a low carbon world. Our rapidly growing team of 130+ people in Austin, TX, Oxford, UK, and Melbourne, Australia brings together exceptionally talented and passionate people in the domains of energy trading, data science, software engineering and renewable energy management.

We have a vacancy for an applied analytics specialists to join our team based in Oxford. This role will solve real world electricity trading and optimisation problems across the domains of Habitat Energy energy trading, data science, and renewable energy management. Your role here should give you the satisfaction of seeing your ideas and solutions result in better outcomes for clients, the company and the climate. Successful colleagues advance quickly in our culture.

This vacancy is for an Applied Data Scientist, will play a critical role in driving the success of our battery storage trading operations across wholesale markets (including day-ahead and intraday), Balancing Mechanism (BM), and ancillary service markets (frequency response and reserve services). You will work with analysts from the Applied Analytics team, and traders from the business to develop market forecasts to improve revenue capture for the batteries under our optimisation, support their productionisation and regularly discuss insights, improvements and conclusions with the rest of the Applied Analytics team and Trading Team.

You will be responsible for:

• Developing market forecasts for our trading teams, who trade the wholesale electricity and ancillary service markets in GB
• Building, prototyping, testing, and scaling parallelised predictive models to forecast electricity market prices, volumes and value across wholesale and ancillary markets
• Cleaning complex datasets, engineering high-value temporal features, and accounting for complex nuances in the electricity market
• Creating actionable insights to improve real world trading performance to maximise revenue and manage risk, going beyond just monitoring model accuracy metrics
• Contributing both ad hoc insights and enduring intelligence to inform trading strategies
• Creating applications to automate the way the batteries we optimise are traded
• Creating insights into risk so traders can understand the range of outcomes of decisions
• Visualising and communicating insights to make it easy for users to assimilate large quantities of insights and quickly make high reward vs risk decisions
• Becoming a specialist on specific areas of the markets we are active in and providing support to colleagues on this topics
• Working with tech teams to source data to underpin your work and help them productionise your applications

Preferred Technical Skills

• Time-series modelling (ARIMA, SARIMA, etc.) and - Tree-based & gradient boosting models (XGBoost, LightGBM, NGBoost)
• Expertise/knowledge of internally stored data & data consumers as well as other data sources that are currently not databased
• Python (3+ for production level code, including pydantic, liniting, type hinting etc)
• Dashboard building (Grafana, Streamlit, Superset, Plotly Dash etc)

Development Lifecycle Skills

• Requirements/Request elicitation and logging (e.g. understanding user needs for models/analysis/outputs etc)
• Scoping of technical work
• Functional prototyping (pre-productionised apps hosted locally or on dev/staging)
• Creating and maintaining documentation to accompany codebases e.g. explanatory methodologies
• Interfacing with technical teams (technical literacy to collaborate smoothly with Core Engineering / Applied Engineering) to support productionisation

Commercial Skills

• Awareness of the drivers of PnL, trade life cycle and associated cashflows in an energy trading and asset optimisation business
• Genuine interest in energy markets and renewable energy solutions

Communication, Presentation and Soft Skills:

• Excellent data organisation, visualisation, story telling, prioritisation of messaging and persuasion of stakeholders
• Adaptability to work in a dynamic, fast-paced trading environment.
• Self-starter/strong initiative with the ability to manage multiple tasks and deadlines.
• Strong presentation skills and the ability to communicate effectively with technical and non-technical audiences.

Optimisation Skills; to further bolster and work with our existing optimisation function and Electricity Trading exeperience, especially associated with BESS will elevate your application for this role:

• Simulation-Optimization Integration
• Stochastic Programming & Robust Optimization
• Virtual environments and package management (poetry/uv)
• Awareness of battery storage technology, including operational characteristics and revenue opportunities.
• Flexibility & Battery Storage (BESS) Revenue Stacking
• Renewable Energy Generation, operations and monetisation (especially solar)
• Energy Storage trading Experience

Tools that you will likely be using:

• Programming: Python, polars, pydantic, uv, SQLAlchemy, Streamlit
• Infrastructure and DB: Postgres, Warehousing (if we did it), prefect, Kubernetes, SQLAlchemy, AWS
• Visualisation: Grafana, Superset, Marimo
• Forecasting + general DS: lightgbm, xgboost, numpy, scipy, scikit-learn
• Claude AI

Ultimately we are looking for someone who is a great fit for our company so we encourage you to apply even if you may not meet every requirement in this posting. We value diversity and our environment is supportive, challenging and focused on the consistent delivery of high quality, meaningful work.

In return, we’ll give you a competitive salary, flexible working arrangements and a lot of personal development opportunities. We operate a hybrid working model in our offices in Oxford and schedule of attendance can be discussed

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