Data Scientist, Expert

Pacific Gas And Electric Company•Oakland, CA

3d•$140,000 - $238,000•Hybrid

About The Position

The Wildfire, Emergency and Operations (WEO) organization is responsible for oversight of PG&E’s wildfire operations and associated mitigations. The organization is responsible for the development and maintenance of consistent processes and work standards associated with sustainable wildfire and emergency response preparedness operations in line with our regulatory policies and practices. Operational Safety, Enterprise Corrective Action Program, Safety Programs, Contractor Safety, and Transportation Safety & Compliance is also embedded within Wildfire, Emergency and Operations. WEO partners with leaders in Energy Delivery and other parts of the business to develop and recommend a strategic direction for emergency preparedness, emergency response and public partnerships. Wildfire, Emergency and Operations is comprised of roughly 1,792 coworkers. 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 location based on business need.

Requirements

Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.
6 years in data science OR no experience, if possess Doctoral Degree or higher, as described above

Nice To Haves

Doctorate Degree in Data Science, Machine Learning, or job-related discipline or equivalent experience
Experience in utility and energy industries
Active participation in the external data science/artificial intelligence/machine learning 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 commonly used data science and/or operations research programming languages, packages, and tools for building data science/machine learning models and algorithms
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
Mastery of the mathematical and statistical fields that underpin data science
Ability to develop, coach, teach and/or mentor others to meet both their career goals and the organization goals

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
Writes and documents reusable python functions and modular python 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 sponsor 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