Description:
We are looking for a Senior AI Research Scientist to expand the horizons of our technology as we work to accelerate the decarbonization of the electricity system. This role involves applied research, and hence the ideal candidate will possess a deep understanding of forecasting techniques, and will develop the knowledge and understanding necessary to apply them to energy markets. They will investigate new technologies to better solve problems we already model, as well as designing solutions to problems we don’t yet solve. In addition, they will work to generalize these solutions so they can be applied more broadly, including on academic datasets for the purpose of sharing with the academic community via publication. A successful candidate will embrace constant learning of both engineering and mathematical concepts, as well as economics and electricity market related topics.
What you might work on
- Develop understanding of mathematics and mechanisms of energy markets in order to be able to discover and adapt appropriate methods to solve specific problems.
- Keep abreast of latest advances in ML research in order to consider if any new methods are especially promising for problems in the electricity market space.
- Generalize and unify problem frameworks across electricity markets.
- Design, perform, and analyze experiments for new ML techniques, as well as consult on those tasks for others on the ML team
- Applying cutting edge ML techniques to problems in the energy space.
- Promote probabilistic thinking and rigor in statistical testing across the company.
- Mentor machine learning engineers on modeling and communication of results (including for publication)
- Author research papers in the fields of generative modeling / forecasting / decision making under uncertainty.
- Write, optimize and review code, with attention to performance and readability.
You might be a good fit if you:
- have at least 5 years of experience conducting (basic or applied) ML research.
- have a strong publication record: NeurIPS, ICLR, ICML; and/or papers on forecasting and generative/probabilistic models in CV/NLP/Speech venues.
- have earned a PhD in ML, statistics or related quantitative modeling field.
- have deep knowledge of math, probability, statistics and algorithms.
- show proven experience researching and implementing relevant machine learning models.
- are able to write robust code in Python.
- are familiar with ML frameworks (like PyTorch or TensorFlow) and libraries (like scikit-learn).
- have outstanding analytical and problem-solving skills.
- have excellent mentorship, communication, and teamwork skills.
- have enthusiasm for learning. Knowledge of the energy industry a strong plus.