March 8, 2019

Pecan Street Seeks Data Analyst

Pecan Street Inc. seeks a Data Analyst to be part of its award-winning team innovating solutions for intelligent energy management and drought mitigation. The selected candidate will work out of Pecan Street’s state-of-the-art, green-built lab in Austin’s Mueller community and will be part of a passionate, dynamic team that works on a range of exciting programs in partnership with public and private entities.

About Pecan Street Inc.

Unlike any other organization, Pecan Street is focused on advancing university research and accelerating innovation in water, energy, and transportation. We provide utilities, technology companies and university researchers access to the world’s best data on consumer energy and water consumption behavior, testing and verification of technology solutions, and commercialization services to help them bring their innovations to market faster.

The solution to our water and energy challenge will require a mixture of new technologies but it will first require a detailed understanding of customers’ water and energy use behavior. Pecan Street’s research addresses technology and behavior and how the two interact. Our ambition is to provide the companies, researchers and organizations that will lead the way toward a more efficient, customer-driven energy and water system, with the research, analysis and insights they need to be successful.

Pecan Street’s lab is a green-built facility where companies of all sizes can develop, test and validate a wide range of products and services. The lab also houses Pecan Street’s secure data center, which powers Dataport – Pecan Street’s interactive web portal to access, visualize, analyze and discuss the organization’s unique data.


  • Provide data science and analytics on large streams of residential energy data, residential water data, home energy audit data and demographic data.
  • Assist in identifying marketable sets of data from within Pecan Street’s data archive for offering to research and commercial partners
  • Using SQL, query Pecan Street data sources to extract information for internal requests of data
  • Coordinate with internal staff on data needs external to the organization, and assist with creating and running queries to fulfill external requests
  • Perform unassisted research towards the goals of the firm
  • Provide analysis and reporting of results

Experience and Skills

Pecan Street seeks a candidate with:

  • Bachelor’s Degree in Engineering, Mathematics, Building Science, Computer Science, Economics, Statistics, or other relevant field. Graduate degree preferred.
  • Proven track record of performing applied research and analysis
  • Understanding of building science and/or residential construction
  • Proficiency in SQL
  • Proficiency in Microsoft Excel
  • Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy
  • Adept at queries, report writing and presenting findings

Professional Qualities

  • Communication and relationship building skills – candidate should be comfortable dealing with all levels of an organization, including delivering presentations to executive level audiences, and community groups.
  • Teamwork – candidate should be able to work effectively in a team environment.
  • Planning and organizational skills – candidate should be able to manage multiple tasks and priorities in a fast paced and demanding work environment.


Benefits are competitive and include:

  • health insurance requiring no contribution from the employee including dental, vision, short- and long-term disability, and affordable family plans;
  • 401(k) retirement planning;
  • 15 days of annual paid-time off plus 10 additional holidays; and,
  • 3 months of fully paid maternity and paternity leave.

Application Process

Applications will be accepted through April 10, 2020. Please apply at Application package should include resume, cover letter, and a report sample detailing the results of a statistical analysis.

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