This position provides principal-level Data Scientist support for the Gas Storage Asset Management team. This role requires the application of data science and analytics to support management of the gas storage well and reservoir assets. This position will also assist with the department strategic objectives of positioning asset data for the future.
Position will require approximately 25% of travel time in work schedule.
This position is hybrid, working from your remote office and your assigned work location based on business need. The assigned work location will be within the PG&E Service Territory (San Ramon, CA).
PG&E is providing the salary range that the company in good faith believes it might pay for this position at the time of the job posting. This compensation range is specific to the locality of the job. The actual salary paid to an individual will be based on multiple factors, including, but not limited to, specific skills, education, licenses or certifications, experience, market value, geographic location, and internal equity. Although we estimate the successful candidate hired into this role will be placed towards the middle or entry point of the range, the decision will be made on a case-by-case basis related to these factors.
A reasonable salary range is:
Bay Area Minimum: $159,000
Bay Area Maximum: $271,000
This job is also eligible to participate in PG&E’s discretionary incentive compensation programs.
Job Responsibilities
- Creates, applies, and evaluates advanced data mining architectures/models/protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets.
- Applies and evaluates data science/machine learning/artificial intelligence methods to develop defensible and reproducible predictive or optimization models that involve multiple facets and iterations in algorithm development.
- Writes and documents complex and reusable Python functions as well as multi-modular Python code for data science.
- As a technical leader, provides thought leadership in the use of ML algorithms for solving business problems.
- Mentors junior data scientists and drives standardization in process and toolsets across the data science community at PG&E.
- Collaborates with analytics platform owners to prioritize and drive development of scalable data science capabilities.
- Acts as peer reviewer for complex models/AI algorithm proposals.
- Recognizes and prioritizes the most important work related to data science models to achieve highest operational and strategic impact for analytics in the business.
- Works with enterprise leaders as an advocate for digital transformation of the business through the adoption of data science, analytics, and data-driven business processes.
- Presents findings and makes recommendations to executive leadership and cross-functional management.
Qualifications
Minimum Qualifications:
- Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics, or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.
- 8 years in data science (or 2 years, if possess Doctoral Degree or higher).
Desired Qualifications:
- Relevant industry (electric or gas utility, renewable energy, analytics consulting, etc.) experience.
- Experience with oil and gas data management and modeling.
- Proficiency managing well data with oil and gas software.
- Experience with underground natural gas storage.
- Experience responding to CalGEM and/or CPUC data requests.
- Experience with GIS data.
Knowledge, Skills, Abilities and (Technical) Competencies
- Thought leadership in the external data science/artificial intelligence/machine learning community of practice, as demonstrated through peer-reviewed journal publications, intellectual property/patent achievements, conference presentations, volunteering in professional organizations for the advancement of the field, participation in externally sponsored research projects, open source contributions, or similar activities.
- Proficiency with data science standards and processes (model evaluation, optimization, feature engineering, etc.) along with best practices to implement them.
- Proficiency with commonly used data science and/or operations research programming languages, packages, and software tools for building data science/machine learning models and algorithms.
- Mastery in explaining in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.
- Ability to clearly communicate complex technical details and insights to colleagues, stakeholders, and leadership.
- Leadership in developing, coaching, teaching and mentoring others to meet both their career goals and the organization goals.
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