Lawrence Berkeley National Lab’s (LBNL) Building Technology and Urban Systems Division has an opening for an Associate Data Scientist to join the team.

In this exciting role, you will assist with writing codes to perform data analytics, analyzing data, and data cleaning. You will perform basic energy modeling, maintain databases and open-source code repositories, assist in development and testing of control sequences, and contribute to literature review. You will work with data from energy meters, HVAC and lighting automation systems, other building sensors, PV systems, batteries, microgrids, and grid signals.You will also assist with writing specifications, reports and journal papers related to these activities.

What You Will Do:

• Work on problems of moderate scope where analysis of data requires understanding of the underlying meaning of the data.
• Exercise judgment within defined procedures and practices to determine appropriate action.
• Develop data-processing methods to assess the quality of the data, and to perform error correction and mitigation.
• Assist with developing statistical models of the data to conduct different analyses (measurement and verification, forecasts, data fusion, control algorithms).
• Contribute and maintain existing open-source code base.
• Write code to build data-driven energy models for commercial buildings.
• Provide data-related assistance to scientists, engineers and stakeholders to conduct research in: microgrids, building controls and analytics, equipment energy efficiency and demand response.
• Analyze large quantities of data, including annual simulation data, historical interval energy data and real-time building performance data. Ensure the data is well maintained in a visualization tool and database developed in collaboration with other project staff.
• Assist with writing specifications, reports and journal papers.

Additional Responsibilities as needed:

• Assist in carrying out more sophisticated statistical analyzes and modeling on the data as needed to support the project.
• Assist with modeling of buildings and microgrids (e.g., energy and semantic models).
• Assist in developing control algorithms to optimize energy use in buildings.
• Interact with other researchers outside of LBNL regarding technical activities of the research.
• Participate in group meetings regarding funding opportunities and research directions.
• Assist Department PIs to identify or respond to internal and external funding opportunities.
• Present the results of the work to internal meetings.

What is Required:

• Bachelor’s Degree in engineering, computer science, or related data-intensive field and a minimum of 2 years of related experience, or an advanced degree without experience, or equivalent work experience.
• Strong experience with data manipulation and processing in Python-Pandas.
• Good experience with other Python packages (sci-kit learn, matplotlib, tensorflow)
• Experience with SkyFoundry’s Axon code language for fault detection and diagnostics (FDD) rule development.
• Experience with Modelica modeling language, modelica results data conversion and analysis.
• Experience with analysis of mechanical systems such as HVAC as well as FDD analysis of these systems.
• Knowledge of semantic modeling and relevant standardized ontologies (BRICK Haystack, etc.).
• Demonstrated experience with software versioning tools (git).
• Demonstrated experience with SQL-based data querying.
• Demonstrated experience with time-series databases.
• Knowledge of building energy efficiency, grid-integrated energy efficient buildings (GEB) and microgrids.
• Excellent written and verbal communication skills.

Desired Qualifications:

• Experience in working in research projects, as well as development, and demonstration in topics related to energy efficiency, semantic modeling and microgrids.
• Knowledge of demand response (especially in California)
• Experience with IoT devices and related software
• Basic knowledge of sensor data acquisition and electrical circuits
• Previous research experience in sustainability, energy efficiency, environmental technology fields
• Experience in writing academic papers or reports

Want to learn more about Berkeley Lab's Culture, Benefits and answers to FAQs? Please visit: https://recruiting.lbl.gov/

Notes:

• This is a full-time, career appointment, exempt (monthly paid) from overtime pay.
• The full salary range for this position is $92,760.00 - $113,388.00. The budgeted salary range that the Lab reasonably expects to pay for this position is $97,914.60 - $108,221.40. Salary offers will be commensurate with the final candidate’s qualification and experience, including skills, knowledge, relevant education, and certifications, and also aligned with the internal peer group.
• This position may be subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
• Work will be primarily performed at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.

Based on University of California Policy - SARS-CoV-2 (COVID-19) Vaccination Program and U.S Federal Government requirements, Berkeley Lab requires that all members of our community obtain the COVID-19 vaccine as soon as they are eligible. As a condition of employment at Berkeley Lab, all Covered Individuals must Participate in the COVID-19 Vaccination Program by providing proof that vaccination requirements have been met or submitting a request for Exception or Deferral. Visit covid.lbl.gov for more information.

Berkeley Lab is committed to Inclusion, Diversity, Equity and Accountability (IDEA) and strives to continue building community with these shared values and commitments. Berkeley Lab is an Equal Opportunity and Affirmative Action Employer. We heartily welcome applications from women, minorities, veterans, and all who would contribute to the Lab's mission of leading scientific discovery, inclusion, and professionalism. In support of our diverse global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status.

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