Job Description The aim of the Wildfire Data Science team in the Wildfire Risk Management organization is to enhance the risk practices of the Electric Operation business and thereby address changing external conditions such as climate change. To this end the Wildfire Data Science team develops and maintains predictive models to enable and close the gap between metrics and electric system performance. These models 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: • Quantification of wildfire mitigation program performance on the distribution and transmission electric system. • Development of causal inference models using Python\PySpark and executed in Foundry or AWS. • Interpretation and representation of meteorological data in models that combine a range of data sources such as the electric system asset data, vegetation, and meteorology. Position Summary Designs, develops, and executes scripts, programs, models, algorithms, and processes, using structured and unstructured data from disparate sources and sizes, generating for defensible, valid, scalable, reproducible and documented machine learning and artificial intelligence models (predictive or optimization) for problem solving and strategy development. Participates in internal and external communities of practice in data science/artificial intelligence/machine learning to advance knowledge in the field. Educates the non-technical community on advantages, risks, and maturity levels of data science solutions. 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 • Architects, develops, and documents reusable functions and modular 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 stakeholder 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 We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements • Bachelor’s Degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field. • Experience in Data Science, 6 years or no experience, if possess Doctoral Degree or higher in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field. Desired: • Doctorate Degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field. • Expertise in experimental design and causal inference methods. • Expertise in statistical methods for time series analysis, statistical modeling, and probabilistic risk assessment. • Relevant industry experience (electric or gas utility, data science consulting, etc.) • Familiarity with the use of supervised, unsupervised, deep learning & physics-based methods for modeling electrical infrastructure failure modes. • Active participation in the external data science/risk assessment/utility community of practice, as demonstrated through volunteering in professional organizations for the advancement of the field, presentations in conferences or publications to disseminate data science knowledge and topics, or similar activities. • Competency with data science standards and processes (model evaluation, optimization, feature engineering, etc) along with best practices to implement them • Knowledge of industry trends and current issues in job-related area of responsibility as demonstrated through peer reviewed journal publications, conference presentations, open source contributions or similar activities • Competency with Agile product development best practices. • Proficiency with Python or Pyspark, code reviews, and code development best practices. • Proficiency in explaining in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines. • Mastery in clearly communicating complex technical details and insights to colleagues and stakeholders