Data Scientist, Expert

Position Summary

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
• 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

Qualifications

• Minimum:
• 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
• Desired:
• 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

Purpose, Virtues and Stands