After obtaining my PhD in physics in 2016, I seamlessly transitioned from nuclear/particle physics to data science. The world of physics may seem distant from data science, but the data generated by high luminosity particle colliders demands cutting-edge tools and statistical methods for insights. I've taken on the full data science pipeline, from designing data-acquiring hardware at the edge to processing and analyzing unstructured, high-velocity data.Currently, I serve as a Principal Data Scientist at Southern California Edison where my work involves leveraging machine learning, GIS, and data engineering to enhance equipment reliability and quantify various avenues of utility risk due to asset failures. I've spearheaded data modernization efforts, performed web scraping, and utilized Natural Language Processing for sentiment analysis. Additionally, I introduced a bias-agnostic, data-driven approach for key performance metrics, and engineered physics-driven features for robust risk models. I have played pivotal roles in enhancing SCE's public safety and risk mitigation as one of the lead data scientists in developing a highly effective Wildfire Risk Reduction Model, contributing significantly to the protection of communities and infrastructure. Additionally, providing probability of failure models of underground equipment for SCE's public safety roadmap, bolstering our risk mitigation efforts to minimize catastrophic underground asset failures.Beyond these achievements, I have a strong track record of creating end-to-end data pipelines, particularly for mitigation specific bespoke transmission asset modeling. Cumulatively these models have had a profound impact on risk quantification, particularly in separate projects like the General Rate Case and the Wildfire Mitigation Program as well as various major projects within SCE. My work ensures informed decision-making, financial planning, and overall success in addressing distinct challenges that come with providing safe, reliable, and clean energy to Southern California.