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Senior Manager, Data Science
2 months ago
We are seeking an experienced Senior Manager, Data Science to lead the AI & Data Science Center of Excellence for Pacific Gas and Electric Company. In this position, you will play a vital role in developing policies, standards, and processes that internal data science teams as well as contracted vendors will comply to for the effective design and development of data science and AI algorithms.
Key Responsibilities- Support data science, AI, and analytics teams across the company by disseminating and supporting the implementation of best practices for machine learning (ML) & AI development.
- Lead the design, implementation, and continuous improvement of governance tools (policies, standards, and processes) for the effective and safe development of ML & AI as a product.
- Act as advisor to the People's Organization (HRBP & Compensation) in the analysis of data science competencies, continuously monitoring the evolution of the industry and advising on skill fitness to current data science work planned enterprise-wide.
- Participate in the internal and external data science community of practice, leading the development of knowledge that advances the field.
- Advise delivery teams in the optimal implementation of advanced technologies as proof of concept, balancing risk and innovation to accomplish business goals.
- Support the identification and implementation of process improvement at the department level.
- Present findings and make recommendations to officers and cross-functional management.
- 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 posses Advanced Degree, as described above).
- 4 years of leadership experience in data science.
- 5 years Utility industry experience.
- Advanced degree in Statistics, Mathematics, Applied Science, Data Science, Engineering, Physics, Economics, or equivalent field.
- Demonstrated track record and understanding of data science and machine learning algorithms (supervised, unsupervised), ML domains (computer vision, NLP, etc.).
- Demonstrated track record in 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.
- Seeing ahead to future possibilities and translating them into breakthrough strategies.
- Ability to clearly and concisely communicate and present complex analysis to both quantitative and non-quantitative audiences.
- Competency in planning and prioritizing work to meet commitments aligned with organizational goals.
- 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).