Manager, Applied Data Science

PG&E Corporation•Oakland, CA

26d•Hybrid

About The Position

The aim of the Applied Data Science team in the Wildfire Mitigation organization is to enhance the risk practices of PG&E's Electric Operation business and thereby address changing external conditions such as climate change. To this end the Applied Data Science team integrates predictive models into PG&E's work planning processes. These models and tools provide a multi-layered view of risk and risk reduction across the electric system so that decision-making processes include and empower employees at all levels of the company to manage risk appropriately. Sample activities include: Development of tools to monitor wildfire mitigation program performance on the distribution and transmission electric system. Development of tools and dashboards to track electric system risk and risk reduction. Support for regulatory filings like the Wildfire Mitigation Plan (WMP). Position Summary Oversees the data science function, which uses predictive modeling, machine learning (ML), optimization, simulation and artificial intelligence (AI) solutions to provide future-focused data driven decisions used by the enterprise. Leads the development of expertise and knowledge in data science, machine learning, mathematics, and statistics to solve specific problems, as well as technology development of large data sets from multiple systems as related to solving those problems. Actively participates in the larger, external community of data science and risk management by monitoring emerging trends and leading strategies to capitalize impact and lower risk. This position is hybrid, working from your remote office and your assigned location based on business need. PG&E is providing the salary range that can reasonably be expected for this position at the time of the job posting. This salary 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, internal equity, specific skills, education, licenses or certifications, experience, market value, and geographic location. The decision will be made on a case-by-case basis related to these factors. This job is also eligible to participate in PG&E's discretionary incentive compensation programs. Bay Area - $159,000 - $236,500

Requirements

Bachelor's degree in Statistics, Mathematics, Applied Science, Data Science, Engineering, Physics, Economics, or equivalent field
2 years hands-on experience in data science (or no experience, if possess Advanced Degree, as described above)
2 years of leadership experience in data science

Nice To Haves

Advanced degree in Statistics, Mathematics, Applied Science, Data Science, Engineering, Physics, Economics, or equivalent field.
Utility industry experience, electric or gas, or other job-related, 3 years
Experience with data science and machine learning algorithms (supervised, unsupervised), ML domains (computer vision, NLP, etc.).
Experience with the following:
a) Statistics: statistical modeling, experimental design, sampling, clustering, data reduction, confidence intervals, testing, modeling, predictive modeling and other related techniques;
b) Artificial Intelligence: machine learning, predictive analytics, as they collect, analyze and extract value out of data; simulation;
c) Software Engineering: programming languages, big data wrangling packages, cloud services, APIs, and related tools.
Making sense of complex, high quantity, and sometimes contradictory information to effectively solve problems.
Ability to clearly and concisely communicate and present complex analysis to both quantitative and non-quantitative audiences.
Ability to apply project management theories, concepts, methods, best practices, and techniques as needed to perform at the job level.
Domain expertise: familiarity with one or more line of business (electric, customer, generation, procurement, gas, risk, etc.) and ability to identify areas where data science can improve processes and inform decision making (this may also include familiarity with the datasets/databases that support these lines of business)

Responsibilities

Manages data scientist teams to accomplish results through effective recruitment and selection, training and development, performance management and coaching, and rewards and recognition.
Works with enterprise leaders to identify and solve business problems requiring the implementation of data science, machine learning and risk management.
Utilizes deep understanding of business drivers and financial levers, along with instrumental data science techniques, to prioritize work and provide strategic decision support.
Ensures compliance with standards and processes to improve quality and timeliness of predictive models and risk optimization tools.
Acts as peer reviewer for code scripts and model development for narrow scope projects. Reviews and approves the maturity for release of technical features in data science products.
Conducts risk-evaluation studies of model impact on business outcomes.
Assesses business implications associated with modeling assumptions, types of inputs, statistical methodologies, programming approach, etc.
Monitors the development of data science, machine learning, artificial intelligence, mathematical modeling and optimization, and similar emerging technologies to continuously assess their impact on business strategies. Participates with peers in risk and maturity assessment of data science and decision tools.
Develops budget (expense, capital, and expenditures) and monitor, forecast and report on budget performance.

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