Specialist II

Geospatial 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.

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/.

• Professional experience in Computer Science, Engineering, or other quantitative discipline
• Geospatial & Remote Sensing Expertise: Proficient in Python (rioxarray, GDAL, rasterio, geopandas, dask) and SQL for geospatial analytics, with hands-on experience in remote sensing (optical, and/or LiDAR, SAR) including preprocessing and derived insights for forestry, and/or wildfire applications.
• Scalable Geospatial Data Processing: Skilled in distributed computing frameworks (Apache Sedona) for large-scale vector and raster data, and experienced with cloud-native formats (GeoParquet, COG, Zarr) and data access via STAC APIs.
• Cloud Data Engineering & Automation: Experienced in building and maintaining automated geospatial data pipelines in AWS (S3, SageMaker, Lambda, Step Functions, Airflow), leveraging event-driven architectures and workflow orchestration.
• Experience applying machine learning techniques to geospatial data for predictive modeling, classification, and anomaly detection. Familiarity with frameworks like PyTorch, TensorFlow, or scikit-learn in geospatial contexts. [Nice to Have]

Benefit packages for this role will start on the 31st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.