Data Scientist Principal

What we're looking for

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 a 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.

8 years in data science OR 2 years, if possess Doctoral Degree or higher, as described above

Desired:

Doctorate Degree in Data Science, Machine Learning, or job-related discipline or equivalent experience

Experience in utility and energy industries

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 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