A global, merchant energy trading firm in Houston seeks a data scientist to support its natural gas & power trading teams. The position will create sophisticated statistical models and algorithms that produce predictive signals that drive trading strategy and commercial decisions. Key responsibilities include:
Extracting, analyzing, and managing data from complex and disparate sources, including data lake.
Applying rigorous machine learning, statistical analysis, regression, and optimization techniques to data to identify signals and predictive patterns.
Generating hypotheses and analyzing data to test and interpret results and to evaluate and test trading strategies.
Developing, testing, and implementing new tools to be used by the trading team.
Working with the Technology team to communicate analytical and modeling results in a dashboard format to Traders; and
Researching and evaluating new technologies/tools associated with Big Data.
Educational and professional requirements include:
Bachelor’s degree (an advanced degree is a plus) in an applied quantitative field (mathematics, physics, computer science, engineering, or statistics).
Excellent Python programming skills and advanced knowledge of SQL.
A strong background in linear regression, probability, scenario modeling, statistics, and time series analysis.
Ability to write and optimize database queries to extract and analyze large data sets.
Approximately 1-5 years of professional experience in Big Data / Data Science / Data Analytics analysis and predictive modeling in the energy industry.
Industry experience in energy trading, upstream/midstream oil & natural gas, or conventional/renewable power. A variety of analytic experiences will be considered.
A commercial mindset and desire to work within a fast-paced end trading floor environment.
Strong communication skills.
Other experience and technical skills (desired, a plus, and not required):
Experience on an energy trading floor is a plus.
Experience and knowledge of the Permian gas region is a plus.
Experience with machine learning for prediction, classification, and anomaly detection is a plus.
Experience with natural language processing (NLP) for analyzing unstructured data is a plus.
Linear programming and non-linear optimization are a plus.