Senior Data Scientist- Flexible Location

Requisition ID # 169750 

Job Category: Accounting / Finance 

Job Level: Individual Contributor

Business Unit: Enterprise Health & Safety

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; Grass Valley; Guerneville; Half Moon Bay; Hayward; Hinkley; Hollister; Holt; 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; Washington; Watsonville; West Sacramento; Wheatland; Whitmore; Willits; Willow Creek; Willows; Windsor; Winters; Woodland; Yuba City

Department Overview

The Reporting & Analytics team of analysts and data scientists within the Business Strategy group of Enterprise Health & Safety is responsible for data engineering, and developing reporting, AI, and Generative AI tools to help mitigate risk in the employee, contractor, and motor vehicle space.

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.

Location is flexible within the PG&E Service Territory, please note hiring leader will make final decision of what are appropriate headquarters for the role based on business need. 

Travel may be required quarterly, or as needed, to attend meetings in the East Bay.

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.

Position duties may include but are not limited to:
•    Risk and regulatory modeling: Develop and maintain data pipelines and risk models that support RAMP/GRC regulatory filings .
•    Program effectiveness analysis: Evaluate program effectiveness using statistical and causal-inference methods.
•    Predictive modeling: Build and deploy predictive models for injury prediction and safety-related outcomes to support proactive risk mitigation and operational decision making.
•    Model monitoring: Implement model monitoring for data drift, label shift, and model performance to ensure reliability of current and future predictive models.
•    Cross-functional collaboration: Partner with safety, operations, and regulatory stakeholders to translate analytical insights into actionable recommendations; clearly communicate findings to technical and non-technical audiences.

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:$126,000
Bay Area Maximum: $200,000

OR

California Minimum:$120,000
California Maximum: $190,000

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

Job Responsibilities
•    Researches and applies knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions
•    Creates 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, 
•    Co-develops mathematical models and AI simulations that represent complex business problems
•     Writes and documents python code for data science (feature engineering and machine learning modeling) independently.
•    Serves as the technical lead for the development of simple models.
•    Develops and presents summary presentations to business.
•    Act as peer reviewer of simple models

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.
•    4 years in data science (or 2 years, if possess Master’s Degree as described above, or no experience, if possess Doctoral Degree or higher, as described above)

Desired Qualifications:
•    Relevant industry (electric or gas utility, renewable energy, analytics consulting, etc.) experience

•    Proficiency in SQL and Python, including core libraries (NumPy, SciPy, pandas, scikit-learn, etc.).
•    Master’s Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.
•    Experience with Git-based version control and modern software development workflows.
•    Experience with natural language processing; exposure to large language models and prompt-engineering concepts a plus.
•    Experience applying analytics in safety, compliance, or risk-related domains a plus.
o    Knowledge, Skills, Abilities and (Technical) Competencie
o    Demonstrated knowledge of and abilities with data science standards and processes (model evaluation, optimization, feature engineering, etc.) along with best practices to implement them
o    Competency in software engineering, statistics, and machine learning techniques as they apply to data science deployment
o    Competency in commonly used data science and/or operations research programming languages, packages, and tools  
o    Hands-on and theoretical experience of data science/machine learning models and algorithms  
o    Ability to synthesize complex information into clear insights and translate those insights into decisions and actions. Demonstrated ability to explain in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.
o    Competency in the mathematical and statistical fields that underpin data science
o    Mastery in systems thinking and structuring complex problems
o    Ability to develop, coach and teach career level data scientists in data science/artificial intelligence/machine learning techniques and technologies