Data Scientist (Staff / Sr Staff) - Power Markets

4 months ago
Full time role
Remote · California, US... more
Remote · California, US... more

Company

Equilibrium Energy is a well-funded, Series A clean energy startup backed by some of the most prominent institutional investors in ...

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Job Description

About our Company

Equilibrium Energy is a well-funded, Series A clean energy startup backed by some of the most prominent institutional investors in climate. We are building a digital native power company operating at the intersection of grid variability, market volatility, economic optimization, commercial structuring, and risk management, across the end-to-end power value chain. Our mission is to accelerate our collective path to climate, energy, and societal equilibriums. Our goal is to become one of the next-generation, digital-native, end-to-end global clean power companies that reshapes the energy industry.

New colleagues will share our vision that a next-generation energy company must be built from the ground up on deep industry expertise combined with an unwavering commitment to modern digital approaches. We design our commercial strategies, operational approaches, and product suites so as to best leverage data-driven insights, automated workflows, ML-infused pipelines, and fully automated decision engines. These capabilities are enabled by our progressively modern software stack and engineering best practices, which in turn provide the scalable platform we need to put a serious dent in carbon emissions. We’re looking for collaborative, talented, passionate and resourceful folks to join our team and help us lay the foundation for our important mission and ambitious plan.

What we are looking for

Equilibrium was founded with a vision for building a company where innovation, collaboration, machine learning, and data science power all aspects of our algorithmic decision-making. We are looking for staff / sr staff data scientists to accelerate the design and delivery of our machine learning models, probabilistic forecasts, and insights dashboards, while helping to shape the science-driven products & processes that will drive the future success of our company. 

As a key member of our sciences group, you will play an active role in a) cultivating our culture of experimentation, insights discovery, and incremental delivery, b) facilitating advancement of our into state of the art models via new feature identification & deep dive event day analysis, c) helping to identify, recruit, train, and mentor members of our growing team of exceptional scientists, and d) partnering with our engineers, product managers, analysts, and commercial team to influence the near to medium term product roadmap.

What you will do

Use data & insights to shape product direction: Influence product and engineering roadmaps through presentation of data-driven recommendations, experimental results, and model performance metrics, in order to evolve organizational best practices, and extract long-term business value. Initiate and lead cross-functional engagements to surface, prioritize, formulate, and structure complex and ambiguous challenges where advanced data science techniques, models, and workflows can have outsized company impact.

Identify new features & data sources: Develop and lead data analysis and feature engineering strategy & discovery. Drive step-change improvements in the information content of our modeling feature stores. Assemble novel datasets to power exploratory analysis, failure mode analysis, new model development, and Equilibrium’s global energy market expansion. 

Apply existing modeling techniques to the energy domain: Formulate business problems as forecasting objectives, define appropriate success criteria, and execute hands-on experimental testing & model training to rapidly assess performance, ultimately leading to delivery into runtime production workflows. 

Performance evaluation: Define, build, and measure a suite of success metrics across our portfolio of candidate and deployed models in order to understand operational characteristics, identify & diagnose sources of under-performance, extract ideas for potential new model features, and power performance tracking dashboards & reports. 

The minimum qualifications you’ll need

  • Passion for clean energy and fighting climate change
  • An advanced degree in computer science, data science, machine learning, artificial intelligence, operations research, engineering, or related quantitative discipline
  • 3+ years experience with python and the supporting computational science tool suite (e.g. numpy, scipy, pandas, scikit-learn, tensorflow, etc.)
  • 2+ years experience in the electricity & energy domain (e.g. electricity price forecasting, congestion prediction etc)
  • 4+ years experience in data science, research science, machine learning, or similar role
  • 2+ years experience with database technologies and sql
  • Experience with probability, hypothesis testing, and uncertainty quantification
  • Experience communicating mathematical concepts, analytical results, and data-driven insights to both technical and non-technical audiences
  • A collaboration-first mentality, with a willingness to teach as well as learn from others

Nice to have additional skills

  • Experience designing and building novel statistical models on time series data, including characterizing probabilistic outcome uncertainty
  • Experience with data visualization and dashboarding technologies (e.g. plot.ly Dash, Streamlit)
  • Experience with model explainability methods (e.g. SHAP)
  • Experience developing, releasing, and tracking performance of ML models in production
  • Experience applying and adapting deep learning, graph neural networks, or reinforcement learning techniques to time series regression or classification problems
  • Experience with dimensionality reduction, component decomposition, or embedding space analysis & visualization techniques (e.g. UMAP, T-SNE, Autoencoder)
  • Experience with optimization techniques (e.g. stochastic optimization, robust optimization)
  • Experience leading and mentoring a team of scientists
  • Demonstrated track record of academic paper or social media publication

Not sure this is the right role for you?

We are a high growth company with accelerating hiring needs so there’s a great chance we’ll be able to create a custom role for you, now or in the future. All roles, titles and compensation packages are tailored to the applicant, so apply anyways and tell us in your cover letter about your dream role. 

What we offer

Equilibrium is composed of deeply knowledgeable industry experts across all our functions, with decades of experience in energy-specific commercial structuring, power systems engineering, machine learning, computational research, operations research, distributed and compute-intensive infrastructure, and modern software & ML engineering. Our experience in the space means we’ve previously built versions of nearly every technical component of our platform. We are now designing them better, and combining them in a holistic and novel way, to achieve global scale and climate impact. We pride ourselves on our deeply empathetic & collaborative culture, honest and direct but respectful communication, and our balanced, flexible, and remote-first work environment. 

Employee benefits include: 

  • Competitive base salary and a comprehensive medical, dental, vision, and 401k package
  • Opportunity to own a significant piece of the company via a meaningful equity grant
  • Unlimited vacation and flexible work schedule
  • Ability to work remotely from anywhere in the United States & Europe, or join one of our regional hubs in Boston, SF Bay Area, or London
  • Accelerated professional growth and development opportunities through direct collaboration and mentorship from leading industry expert colleagues across energy and tech

Equilibrium Energy is a diverse and inclusive, equal opportunity employer that does not discriminate on the basis of race, gender, nationality, sexual orientation, veteran status, disability, age, or other legally protected status.

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