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Quantitative Scientist (Staff / Sr Staff) - Power Markets

Equilibrium Energy

Full-time
USA
python
sql
machine learning
trading
artificial intelligence
Apply for this position

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 quantitative scientists with a heavy focus on trading strategy research and development in the energy space to accelerate the maturation of our quantitative trading business, while helping to shape the science-driven products, tools, & 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, signal discovery, and incremental delivery, b) facilitating research into profitable strategy development and energy market dynamics, c) helping to identify, recruit, train, and mentor members of our growing team of exceptional trading analysts, and d) partnering with our engineers, product managers, scientist, and commercial team to influence the near to medium term product roadmap.

What you will do

Use research insights to shape product direction: Influence product and engineering roadmaps through presentation of research insights, experimental results, and trading strategy backtest metrics, in order to evolve organizational direction. Initiate and lead cross-functional engagements to surface, prioritize, formulate, and structure complex and ambiguous challenges where advanced novel quantitative analytics or machine learning techniques can drive outsized company trading impact.

Trading performance analytics: Investigate driving factors in trading over/under performance, identifying learnings, and drive a fly-wheel of continuous strategy improvement and team education.

Research, develop, backtest, and deploy novel quantitative trading signals and strategies in the energy domain: Identify and extract sources of trading alpha by researching multiple energy datasets, surveying industry techniques & domain intuition, and executing hands-on experimental models & backtests. Drive the design, specification, development, and production deployment of our suite of novel quantitative trading solutions. Lead short to medium term research projects that advance the state-of-the-art in quantitative research techniques, as applied to energy asset management and financial trading.

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

  • 6+ years experience in quantitative research & analytics, automated systematic financial trading, or similar role, in the US Power Markets

  • 4+ years experience with python and the supporting computational science tool suite (e.g. numpy, scipy, pandas, scikit-learn, tensorflow, etc)

  • Experience in the electricity and energy domain (e.g. wholesale market prices)

  • Experience developing, releasing, and tracking performance of quantitative trading strategies in production

  • 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 database technologies and sql

  • Experience with probability, hypothesis testing, and uncertainty quantification

  • Experience with optimization techniques (e.g. stochastic optimization, robust optimization)

  • Experience with data visualization and dashboarding technologies (e.g. plot.ly Dash, Streamlit)

  • Experience leading and mentoring a team of analysts

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About the job

Full-time
USA
Posted 18 hours ago
python
sql
machine learning
trading
artificial intelligence

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Quantitative Scientist (Staff / Sr Staff) - Power Markets

Equilibrium Energy

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 quantitative scientists with a heavy focus on trading strategy research and development in the energy space to accelerate the maturation of our quantitative trading business, while helping to shape the science-driven products, tools, & 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, signal discovery, and incremental delivery, b) facilitating research into profitable strategy development and energy market dynamics, c) helping to identify, recruit, train, and mentor members of our growing team of exceptional trading analysts, and d) partnering with our engineers, product managers, scientist, and commercial team to influence the near to medium term product roadmap.

What you will do

Use research insights to shape product direction: Influence product and engineering roadmaps through presentation of research insights, experimental results, and trading strategy backtest metrics, in order to evolve organizational direction. Initiate and lead cross-functional engagements to surface, prioritize, formulate, and structure complex and ambiguous challenges where advanced novel quantitative analytics or machine learning techniques can drive outsized company trading impact.

Trading performance analytics: Investigate driving factors in trading over/under performance, identifying learnings, and drive a fly-wheel of continuous strategy improvement and team education.

Research, develop, backtest, and deploy novel quantitative trading signals and strategies in the energy domain: Identify and extract sources of trading alpha by researching multiple energy datasets, surveying industry techniques & domain intuition, and executing hands-on experimental models & backtests. Drive the design, specification, development, and production deployment of our suite of novel quantitative trading solutions. Lead short to medium term research projects that advance the state-of-the-art in quantitative research techniques, as applied to energy asset management and financial trading.

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

  • 6+ years experience in quantitative research & analytics, automated systematic financial trading, or similar role, in the US Power Markets

  • 4+ years experience with python and the supporting computational science tool suite (e.g. numpy, scipy, pandas, scikit-learn, tensorflow, etc)

  • Experience in the electricity and energy domain (e.g. wholesale market prices)

  • Experience developing, releasing, and tracking performance of quantitative trading strategies in production

  • 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 database technologies and sql

  • Experience with probability, hypothesis testing, and uncertainty quantification

  • Experience with optimization techniques (e.g. stochastic optimization, robust optimization)

  • Experience with data visualization and dashboarding technologies (e.g. plot.ly Dash, Streamlit)

  • Experience leading and mentoring a team of analysts

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