Requisition ID # 161031 
Job Category: Accounting / Finance 
Job Level: Individual Contributor
Business Unit: Gas Engineering
Work Type: Hybrid
Job Location: San Ramon
 
 Department Overview
The Risk Management team is responsible for overseeing the enterprise risk management practices within the Gas functional area.  The team works closely with risk owners in asset management and operation groups to provide support to understand gas risks. The team also collaborates with the Enterprise Operations and Risk Management team to continue to mature and improve risk management practices within the company.
 
Position Summary
The successful candidate will be responsible for leading development and building statistical models, data analysis and generating visualizations using several analytical tools such as Python, R, Amazon Web Services (AWS), and Tableau to better understand, predict and manage PG&E’s risk in Gas. This position will combine asset performance research using internal and external data, along with financial information and risk management analysis. This position will also interact with other stakeholders outside the risk management department to facilitate the acquisition of data, determine its quality, and engage the risk owners and stakeholders on the development of quantitative risk models. As an Expert Data Scientist, it will also be required to effectively communicate the results and the model calculations to various PG&E personnel with different backgrounds (technical and non-technical) and to train other data scientists to do the same.
 
 
Position duties may include (but are not limited)-

Lead development and maintenance of quantitative risk models in support of enterprise risk management activities and regulatory proceedings.
Collaborate with Gas Operations risk owners and stakeholders to better understand their risks, available data, and provide solutions for the best modeling techniques.
Use risk model outcomes to support the portfolio prioritization process.
Be able to effectively communicate how the quantitative risk models work and explain the results to risk owners and the leadership team.
Create visualizations using R, Python or Tableau to communicate risk models results.
Extract useful statistics and insight from asset performance data to drive/support the quantitative risk assessment process.
Assess business implications associated with modeling and supports subject matter experts in the application of potential 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 PG&E Service Territory (San Ramon, CA)
 
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.​
 
A reasonable salary range is:​
 
Bay Area Minimum:$136,000
Bay Area Maximum:$232,000
 
This job is also eligible to participate in PG&E’s discretionary incentive compensation programs.
 
 
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)

Desired:

Excellent written and oral communication skills for coordinating across teams and effective communication of risk model assumptions/results.
Demonstrated collaboration or paired development work history.
Advanced Excel/Visual Basic skills.
A strong understanding of one or several analysis and programming packages such as R, SAS, or Python (with working knowledge of Pandas, SciPy, Numpy, IPython).
Deep knowledge of applied statistics including complex multivariate statistical analysis, Bayesian statistics, Time Series analysis (e.g Autoregressive models).
Proven proficiency in developing and implementing predictive models.
Experience and interest in data visualization techniques. Ability to convey complex analyses with the most efficient and intuitive visual methods and the ability to effectively communicate findings.
A passion and curiosity for data and data-driven decision making.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applying these techniques to make business decisions.
Ability to teach and/or mentor junior colleagues.
Experience querying databases and using statistical computer languages: R, Python, SQL, etc.
Experience with quantitative risk modeling specific to natural gas (gas dispersion, flare radiation, fireball, etc).
Gas Operations knowledge.
Familiarity with gas transmission and/or gas distribution flow models.