Expert Data Scientist- Flexible Location
Requisition ID # 102960
Job Category : Accounting / Finance
Job Level : Individual Contributor
Business Unit: Gas Operations
The Risk Management team is responsible for overseeing the enterprise risk management practices within Gas Operations (GO). The team works closely with risk owners in asset management and operation groups to provide support to understand GO 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.
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 Operations. 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, you 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 has flexibility to be based anywhere in PG&E Service Territory (hiring leader has final approval of headquarters based on business need). Individual must live in PG&E Service Territory to perform duties.
- 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.
- Bachelor’s Degree in Computer Science, Econometrics, Economics, Engineering, Mathematics, Applied Sciences, Statistics or job-related discipline or equivalent experience.
- Job-related experience (e.g. data analytics and modeling), 8 years, OR Master’s Degree and job-related experience, 6 years, OR Doctorate and job-related experience, 3 years
- 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.