Senior Research Scientist

Published
January 14, 2022
Location
Houston, TX
Category
Job Type

Description

At NRG, we’re bringing the power of energy to people and organizations by putting customers at the center of everything we do. We generate electricity and provide energy solutions and natural gas to millions of customers through our diverse portfolio of retail brands. A Fortune 500 company, operating in the United States and Canada, NRG delivers innovative solutions while advocating for competitive energy markets and customer choice, working towards a sustainable energy future. More information is available at www.nrg.com. Connect with NRG on Facebook, LinkedIn and follow us on Twitter @nrgenergy.

Summary:

At NRG, we apply advanced analytics and modeling to improve business results. Within our NRG Retail business, which includes Reliant Energy, we aim to promote customized offerings: the right product offered through the right channel, with the right message at the right time for each current or prospective customer. To accomplish this, we leverage our data via predictive modeling, statistical analyses, and optimization. If you love data, quantitative modeling, and social science, you will fit right in.

We are currently looking to hire a Senior Research Scientist in our Retail Data Analytics group to apply machine learning and Bayesian statistics to develop pricing models; design and analyze experiments; and research and apply appropriate quantitative methods to a wide variety of analyses and modeling.

Responsibilities:

Essential Duties/Responsibilities:
- Apply statistical modeling or machine learning or optimization methods to optimize marketing efforts with respect to customer acquisition, retention, attrition, and pricing

- Research best practices and advice across the Retail Data Analytics team on analytical methods and modeling

- Develop algorithm or software when necessary to implement useful quantitative methods found in academic research
 

Qualifications:

Education:
-Bachelor’s degree in a quantitative field, such as Statistics, Mathematics, Computer Science, Economics, Engineering, Operations Research, or Industrial Engineering required.

-Advanced Degree (MS or PhD) in Statistics, Mathematics or Quantitative Marketing with a focus on machine learning and/or Bayesian Statistics is strongly preferred.

Experience:

- 3+ years of experience in statistical modeling and quantitative analysis in industry or full time academic research

- Research or industry experience with pricing strongly preferred
 

Technical Skills:

- Quantitative modeling, using machine learning and Bayesian Statistics

- Bayesian multilevel regression modeling with partial pooling

- Instrument variables, two-stage least squares regression, differences-in-differences

- Optimization under uncertainty

- Survival modeling

- Modeling and inference with panel data and time series data

- Proficient programming skills with Python, R, or Stata

Additional Knowledge, Skills and Abilities:

- Ability to translate complex business issues into achievable analytical learning objectives and actionable analytic projects

- Ability to learn and apply new quantitative techniques quickly and appropriately
- Ability to interpret complex analytic results and develop practical business implications
- Good communication skills

- Keen attention to detail
- Ability to work as a team member in a fast-paced environment
- Think critically about analyses to ensure the conclusions make sense before sharing

NRG Energy is committed to a drug and alcohol free workplace.  To the extent permitted by law and any applicable collective bargaining agreement, employees are subject to periodic random drug testing, and post-accident and reasonable suspicion drug and alcohol testing. EOE AA M/F/Protected Veteran Status/Disability

EEO is the Law Poster (The poster can be found at http://www.eeoc.gov/employers/upload/poster_screen_reader_optimized.pdf)

Level, Title and/or Salary may be adjusted based on the applicant's experience or skills.

Official description on file with Human Resources

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