- અનુભવ
- 2–8 yrs
- પગાર
- —
- ઓપનિંગ્સ
- 1
- પોસ્ટ કર્યું
- 11 કલાક પેહલા
- કાર્ય મોડ
- ઓફિસમાં
- શિક્ષણ
- માસ્ટર ડિગ્રી
- લાયકાત
- Applicants with a master’s degree in one of the listed technical disciplines and the required data science experience are eligible; candidates with a doctoral degree or higher may qualify with 2 years of experience. A doctorate is preferred but not mandatory.
- ફરી શરૂ કરો
- અરજી કરવી જરૂરી છે
તમે ક્યાં કામ કરશો
કામનું વર્ણન
Role Overview
The Undergrounding Risk Management group within Undergrounding & System Hardening works to strengthen risk management practices for Electric Operations and help the organization respond to changing external pressures, including climate-related impacts. The Electric Risk Management & Analytics team builds, maintains, and applies predictive models that narrow the gap between performance metrics and actual electric system behavior. These models give the business a layered view of system risk and risk reduction so employees across the company can make better-informed decisions.
What You’ll Work On
- Measure how wildfire mitigation programs are performing across distribution and transmission systems.
- Build predictive models in Python or PySpark and run them in Foundry or AWS.
- Analyze and incorporate meteorological inputs alongside asset, vegetation, and other utility data into models.
- Design statistical approaches and build programmatic solutions that turn risk model outputs into practical business tools.
- Develop scripts, programs, models, user interfaces, algorithms, and workflows that handle both structured and unstructured data from multiple sources.
- Create defensible, scalable, reproducible, and well-documented machine learning and AI models for prediction or optimization.
- Help non-technical stakeholders understand what data science solutions can do, where their limits are, and how mature they are.
- Apply advanced data science methods to support business decisions and identify patterns in complex datasets.
- Build data mining structures, statistical reporting, and analytical methods to surface trends in mixed data.
- Extract, transform, and load data from varied sources for feature engineering and model development.
- Prepare and wrangle data for machine learning pipelines.
- Develop reusable functions and modular code for data science work.
- Evaluate how modeling choices, inputs, implementation details, and analytical processes affect business outcomes.
- Partner with stakeholders and subject matter experts to identify useful data science opportunities.
- Present conclusions and recommendations to senior leadership.
- Serve as a peer reviewer for complex models.
Required Background
- Master’s degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or a closely related field.
- At least 8 years of experience in data science; or 2 years of experience if you hold a doctoral degree or higher in one of the listed fields.
Preferred Qualifications
- Doctorate degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or a related discipline.
- Strong ability in experimental design and causal inference.
- Deep knowledge of time series analysis, statistical modeling, and probabilistic risk assessment.
- Experience in utility, energy, or data science consulting environments.
- Understanding of supervised, unsupervised, deep learning, and physics-based approaches for electrical infrastructure failure modeling.
- Solid command of data science best practices such as model evaluation, optimization, and feature engineering.
- Awareness of current industry developments shown through journal publications, conference talks, open-source work, or similar contributions.
- Comfort working with Agile product development practices.
- Strong Python or PySpark development skills, including code reviews and disciplined coding practices.
- Ability to explain statistical inference, machine learning concepts, software engineering topics, and deployment pipelines in depth.
- Excellent communication skills for translating technical work to colleagues and stakeholders.
- Capability to coach, mentor, and support the development of others.
Additional Information
Top priorities for this role include PySpark proficiency, user interface development experience, and strong cross-functional collaboration skills.
Location: Oakland, CA.
This is a contract position based onsite.
No stipend or salary amount was provided in the source.