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Job Description

MLOPs Engineer

The aim of the Wildfire Data Science team in the Wildfire Risk Management organization is to enhance the risk practices of PG&E’s 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 PG&E to 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 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 [email protected] 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

• Professional experience developing and designing Machine Learning Technologies and systems

• Proficiency in Python and ML Frameworks like Tensor Flow or PyTorch

• Practical experience with cloud infrastructure AWS Sagemaker (s3, Lambda, Glue) and/or Snowflake and Palantir Foundry.

• Experience with containerization and container orchestration tools like Docker, Kubernetes,

Experience with Docker to create reproducible environments for data processing and model deployment.

• Proficiency with software engineering best practices: version control, testing and CI/CD automation (e.g., GitHub, Jenkins)

• Experience implementing model versioning, deployment, monitoring and observability systems.

• Experience with Remote Sensing data and geospatial technologies. [Nice to Have]

• Experience with deep learning at scale with geospatial remote sensing data [Nice to Have]

• Ability to manage the end-to-end ML lifecycle across platforms (e.g. Palantir Foundry and AWS SageMaker (Nice to Have)