Invenia is looking for a Data Scientist who will collaborate closely with our research team to explore and understand electricity grid related data. We will rely on you to ask questions, connect the dots, and uncover interesting and surprising relationships between time series, geographic data, and other diverse and complex types of data. Much of this work will involve analyzing data on past events to identify relationships and driving factors, and to then determine which features could be used to increase forecasting accuracy of future events.
From creating experiments and prototyping implementations, to testing and deploying promising ideas quickly and safely, our researchers work on real-world problems with a focus on understanding and improving the efficiency of electricity grids. They are also active in the wider research community by partnering with universities, publishing research papers, and attending conferences.
- Work with the research team using statistical techniques, machine learning tools, and visualization tools to organise, clean, interlink, analyse, explore, and interpret data.
- Work within individual research projects to propose new features and forecasting models.
- Work with the Infrastructure team to prioritize new sources of data and develop requirements for storing and connecting various forms of data.
- Communicate results in the form of discussions and reports to other team members.
- Collaborate with and maintain relationships with external research labs, publish research papers, and attend conferences.Keep current with technical and industry developments.
- STEM undergraduate or graduate degree.
- Familiarity with machine learning and statistics.
- Excellent interpersonal, verbal, written, and presentation skills.
- Knowledge of SQL and R/Python.
Additional Desirable Experience
- Proven experience as a Data Scientist or Data Analyst.
- Remote, asynchronous communication across multiple time zones.
- Experience with Julia and Geographic Information Systems (GIS).
- Experience with Bayesian methods, probabilistic modelling, and statistical inference on complex data.
- Knowledge of the electricity sector.