Data Scientist, Expert

Requisition ID# 164706

Job Category: Accounting / Finance

Job Level: Individual Contributor

Business Unit: Operations - Other

Work Type: Hybrid

Job Location: Oakland; Alameda; Alta; American Canyon; Angels Camp; Antioch; Auberry; Auburn; Avenal; Avila Beach; Bakersfield; Balch Camp; Bay Point; Bear Valley; Belden; Bellota; Belmont; Benicia; Berkeley; Brentwood; Brisbane; Buellton; Burney; Buttonwillow; Calistoga; Campbell; Canyon Dam; Canyondam; Capitola; Caruthers; Chico; Clearlake; Clovis; Coalinga; Colusa; Concord; Concord; Corcoran; Cottonwood; Cupertino; Daly City; Danville; Davis; Dinuba; Downieville; Dublin; Emeryville; Eureka; Fairfield; Folsom; Fort Bragg; Fortuna; Fremont; French Camp; Fresno; Fresno; Fulton; Garberville; Geyserville; Gilroy; Goodyear; Grass Valley; Guerneville; Half Moon Bay; Hayward; Hinkley; Hollister; Holt; Houston; Huron; Jackson; Kerman; King City; Lakeport; Lemoore; Lincoln; Linden; Livermore; Lodi; Loomis; Los Banos; Lower Lake; Madera; Magalia; Manteca; Manton; Mariposa; Martell; Marysville; Maxwell; Menlo Park; Merced; Meridian; Millbrae; Milpitas; Modesto; Monterey; Montgomery Creek; Morgan Hill; Morro Bay; Moss Landing; Mountain View; Napa; Needles; Newark; Newman; Novato; Oakdale; Oakhurst; Oakley; Olema; Orinda; Orland; Oroville; Palo Alto; Palo Cedro; Paradise; Parkwood; Paso Robles; Petaluma; Pioneer; Pismo Beach; Pittsburg; Placerville; Pleasant Hill; Point Arena; Potter Valley; Quincy; Rancho Cordova; Red Bluff; Redding; Richmond; Ridgecrest; Rio Vista; Rocklin; Roseville; Round Mountain; Sacramento; Salida; Salinas; San Bruno; San Carlos; San Francisco; San Francisco; San Jose; San Luis Obispo; San Mateo; San Rafael; San Ramon; San Ramon; Sanger; Santa Cruz; Santa Maria; Santa Nella; Santa Rosa; Selma; Shaver Lake; Sonoma; Sonora; South San Francisco; Springville; Stockton; Storrie; Taft; Tracy; Turlock; Twain; Ukiah; Vacaville; Vallejo; Walnut Creek; Wasco; Watsonville; West Sacramento; Wheatland; Whitmore; Willits; Willow Creek; Willows; Windsor; Winters; Woodland; Yuba City

Department Overview

The aim of the Wildfire Data Science team in the Wildfire Risk Management organization is to enhance the risk practices of PG&E's Electric Operation business and thereby address changing external conditions such as climate change. To this end the Wildfire Data Science team develops and maintains predictive models to enable PG&E to close the gap between metrics and electric system performance. These models provide a multi-layered view of risk and risk reduction across the electric system so that decision-making processes include and empower employees at all levels of the company to manage risk appropriately.
Sample activities include:

• Quantification of wildfire mitigation program performance on the distribution and transmission electric system.
• Development of causal inference models using PySpark and executed in Foundry or AWS.
• Interpretation and representation of meteorological data in models that combine a range of data sources such as the electric system asset data, vegetation, and meteorology.
  

Position Summary

Designs, develops, and executes scripts, programs, models, algorithms, and processes, using structured and unstructured data from disparate sources and sizes, generating for defensible, valid, scalable, reproducible and documented machine learning and artificial intelligence models (predictive or optimization) for problem solving and strategy development. Participates in internal and external communities of practice in data science/artificial intelligence/machine learning to advance knowledge in the field. Educates the non-technical community on advantages, risks, and maturity levels of data science solutions.

• 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.

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.

Bay Minimum: $140,000
Bay Maximum: $238,000

&/OR

CA Minimum: $133,000
CA Maximum: $226,000

This job is also eligible to participate in PG&E's discretionary incentive compensation programs.

Job Responsibilities

• Researches and applies advanced knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions.
• Creates advanced data mining architectures / models / protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets
• Extracts, transforms, and loads data from dissimilar sources from across PG&E for their machine learning feature engineering
• Applies 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.
• Wrangles and prepares data as input of machine learning model development and feature engineering
• Architects, develops, and documents reusable functions and modular code for data science.
• Assesses business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures and processes, and advanced data analysis.
• Works with stakeholder departments and company subject matter experts to understand application and potential of data science solutions that create value.
• Presents findings and makes recommendations to senior management.
• Act as peer reviewer of complex models

Qualifications

Minimum:

• Bachelor's Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.
  Experience in Data Science, 6 years OR no experience, if possess Doctoral Degree or higher in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.

Desired:

• Doctorate Degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field.
• Expertise in experimental design and causal inference methods.
• Expertise in statistical methods for time series analysis, statistical modeling, and probabilistic risk assessment.
• Relevant industry experience (electric or gas utility, data science consulting, etc.)
• Familiarity with the use of supervised, unsupervised, deep learning & physics-based methods for modeling electrical infrastructure failure modes.
• Active participation in the external data science/risk assessment/utility community of practice, as demonstrated through volunteering in professional organizations for the advancement of the field, presentations in conferences or publications to disseminate data science knowledge and topics, or similar activities.
• Competency with data science standards and processes (model evaluation, optimization, feature engineering, etc) along with best practices to implement them
• Knowledge of industry trends and current issues in job-related area of responsibility as demonstrated through peer reviewed journal publications, conference presentations, open source contributions or similar activities
• Competency with Agile product development best practices.
• Proficiency with Python or Pyspark, code reviews, and code development best practices.
• Proficiency in explaining in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.
• Mastery in clearly communicating complex technical details and insights to colleagues and stakeholders
• Ability to develop, coach, teach and/or mentor others to meet both their career goals and the organization goals