Data Scientist, Expert

PG&E

United States

United States

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Requisition ID# 170115 Job Category: Accounting / Finance Job Level: Individual Contributor Business Unit: Electric Operations Work Type: Hybrid Job Location: Oakland Department Overview The New Business PMO (NB PMO) is the budget and process owners for Gas and Electric Distribution New Business. Its mandate is to provide end-to-end financial and program management, process ownership, and timely delivery of the New Business customer work portfolio (currently approximate 20k distribution projects and approximately annual $1.5B capital budget across the PG&E Territory). The NB PMO works closely with Service Planning & Design (SP&D), which owns the customer-facing experience. The NB PMO uses the production system to ensure customer work is efficiently following through intake, design, dependencies, scheduling, execution and construction. Together, supporting PG&E's True North Strategy by ensuring work is delivered on time, on budget, and with a positive customer impact. 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 Bay Area of 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 This job is also eligible to participate in PG&E's discretionary incentive compensation programs. Job Responsibilities Responsibilities typically include the following but not limited to, with ensuring delivering on achieving program metrics/objectives. Researches and applies advanced knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions. Solves unique and complex strategic issues and problems, while developing and monitoring budgets, forecast models, dashboards, presentations, and ad hoc analysis. 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. Collaborates with analytics platform owners to prioritize and drive development of scalable data science capabilities. Works with sponsor departments and company subject matter experts to understand application and potential of data science solutions that create value. 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 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 Relevant industry (electric or gas utility, renewable energy, analytics consulting, etc.) experience 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

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