Job Location: United States
Posting Title
Data Scientist
Location
CO – Golden
Position Type
Regular
Hours Per Week
40
COVID-19 Safety Protocols
Employment at NREL is contingent upon your compliance with all NREL and U.S. Department of Energy (DOE) safety protocols and mitigation efforts directed at the COVID-19 pandemic.
Working at NREL
The National Renewable Energy Laboratory (NREL), located at the foothills of the Rocky Mountains in Golden, Colorado is the nation’s primary laboratory for research and development of renewable energy and energy efficiency technologies.
From day one at NREL, you’ll connect with coworkers driven by the same mission to save the planet. By joining an organization that values a supportive, inclusive, and flexible work environment, you’ll have the opportunity to engage through our eight employee resource groups, numerous employee-driven clubs, and learning and professional development classes.
NREL supports inclusive, diverse, and unbiased hiring practices that promote creativity and innovation. By collaborating with organizations that focus on diverse talent pools, reaching out to underrepresented demographics, and providing an inclusive application and interview process, our Talent Acquisition team aims to hear all voices equally. We strive to attract a highly diverse workforce and create a culture where every employee feels welcomed and respected and they can be their authentic selves.
Our planet needs us! Learn about NREL’s critical objectives, and see how NREL is focused on saving the planet.
Note: Research suggests that potential job seekers may self-select out of opportunities if they don’t meet 100% of the job requirements. We encourage anyone who is interested in this opportunity to apply. We seek dedicated people who believe they have the skills and ambition to succeed at NREL to apply for this role.
Job Description
The Data, Analysis, and Visualization Group in the NREL Computational Science Center has an opening for a full-time Data Scientist in applied predictive modeling, data analysis, and visualization with an emphasis in machine learning techniques, streaming data analytics, and probabilistic modeling.
NREL is looking for a dynamic, motivated researcher with a strong technical background and an interest in the mission of NREL. The successful candidate will collaborate with NREL staff and researchers, other national laboratories, and universities on efforts to develop data science solutions at scale to real-world problems in renewable energy research. In addition to existing skills, candidates should demonstrate a high degree of curiosity, willingness to learn new skills, and ability to adapt to the data needs of differing domains. In addition, a successful candidate will have demonstrated research experience, a strong record of publication, and experience leading technical tasks.
Specific Projects Relevant To This Position Include
- Multi-dimensional (space and time) and multi-modal sensor data and track fusion using time sequences Bayesian techniques preserving data uncertainty estimates (Kalman filtering) experience preferred. Use of CARLA, SUMO, and other software frameworks common in AV development and roadway operational analysis preferred.
- Bayesian modeling, prediction, and simulation of wildlife activities at the meter to 50km scale. Utilizing GPS and other telemetry data to construct, calibrate, and validate such models.
Basic Qualifications
For Researcher II Level
Relevant Master’s Degree . Or, relevant Bachelor’s Degree and 2 or more years of experience . General knowledge and application of scientific technical standards, principles, theories, concepts and techniques. Training in team, task or project leadership responsibilities. Intermediate abilities and knowledge of practices and techniques. Beginning experience in project management. Good technical writing, interpersonal and communication skills.
For Researcher III level
Relevant PhD . Or, relevant Master’s Degree and 3 or more years of experience . Or, relevant Bachelor’s Degree and 5 or more years of experience . Demonstrates complete understanding and wide application of scientific technical procedures, principles, theories and concepts in the field. General knowledge of other related disciplines. Demonstrates leadership in one or more areas of team, task or project lead responsibilities. Demonstrated experience in management of projects. Very good technical writing, interpersonal and communication skills.
Additional Required Qualifications
- Experience with uncertainty quantification
- Experience working with large and complex real-world datasets.
- Experience programming in Python on diverse applications.
Preferred Qualifications
- Research-focused doctoral degree or equivalent research experience.
- Experience proposing, conducting, and publishing research.
- A strong familiarity with Unix/Linux operating systems and open-source software, preferably in a production data-center cloud or HPC environment.
- Familiarity with foundational statistical and machine learning concepts such as regression models, uncertainty quantification, Bayesian analysis, model selection, clustering, outlier detection, etc.
- Experience in data management on diverse data sets including experience in designing efficient and robust ETL pipelines. Specifically, loading, filtering, organizing, transforming, and generally preparing large datasets for scientific analysis.
- Sufficient software engineering expertise to enable production-quality solutions: object-oriented design, coding, and testing patterns; engineering software platforms and large-scale data infrastructures. Interested candidates should have experience programming Python and R (optionally, in addition to other languages).
- Experience with scientific plotting libraries (e.g., plot.ly, matplotlib, bokeh, ggplot) both for research data analysis and for explanatory visualization in publications and presentations.
- Background in relevant engineering disciplines especially vehicle technologies, power systems, grid, smart cities and campuses, and IOT.
- Candidates should be able to demonstrate some existing skills and experience in applying data science in industry, academia, or government settings.
- Bayesian predictive modeling and classification using machine learning methods.
Annual Salary Range (based on full-time 40 hours per week)
Job Profile: Researcher III / Annual Salary Range: $75,500 – $135,900
Job Profile: Researcher II / Annual Salary Range: $69,400 – $114,500
NREL takes into consideration a candidate’s education, training, and experience, as well as the position’s work location, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee’s salary history will not be used in compensation decisions.
Benefits Summary
Benefits include medical, dental, and vision insurance; short*- and long-term disability insurance; pension benefits*; 403(b) Employee Savings Plan with employer match*; life and accidental death and dismemberment (AD&D) insurance; personal time off (PTO) and sick leave; paid holidays; and tuition reimbursement*. NREL employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement. Limited-term positions are not eligible for long-term disability or tuition reimbursement.
- Based on eligibility rules
Submission Guidelines
Please note that in order to be considered an applicant for any position at NREL you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.
EEO Policy
NREL is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
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