Matthew Mendoza

Data Scientist, Predictive Analytics Data Science, Sr Advisor @Southern California Edison (SCE)

Fontana, CA, US
EMAILS
m••••••••@sce.com
MOBILE NUMBERS
+15•••••••80

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WORK HISTORY

Jun 2022 — Present

Data Scientist, Predictive Analytics Data Science, Sr Advisor @Southern California Edison (SCE)

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Pomona, CA, US

Principal Data Scientist with Data Science and Advanced Analytics. Predictive analytics, machine learning, deep learning, and advanced analytics for asset management, wildfire risk reduction, and asset strategy.

EDUCATION

2006 — 2008

University of California, Riverside

M.S., Physics

2002 — 2006

University of California, Santa Cruz

B.S., Physics

2008 — 2016

University of California, Riverside

Doctor of Philosophy (Ph.D.), Physics

SKILLS

LinuxStatistical Data AnalysisMachine LearningScientific ComputingCProgrammingResearchParticle PhysicsScientific WritingModelingData AnalysisSimulationsParallel ComputingSocial MediaData MiningDecision TreesMusicScienceRLabviewMathematicaMicrosoft OfficeGuitarFourier AnalysisManagementAlgorithmsRootMultivariate AnalysisLinear RegressionC++PythonPredictive AnalyticsPerlPhysicsLatexEvent PlanningData ScienceStatistical ModelingMathematical ModelingCharacterization

ABOUT MATTHEW MENDOZA

With a PhD in Physics obtained in 2016, I transitioned from the realm of nuclear and particle physics to the dynamic field of data science, where the intersection of complex data and innovative analytical techniques is crucial. The high-volume data produced by particle colliders requires advanced statistical methods and cutting-edge tools—insights gained from this background serve as the foundation for my work today.As a Data Scientist at Southern California Edison, I leverage my expertise in machine learning, GIS, and data engineering to enhance equipment reliability and assess various risk factors associated with utility asset failures. Over my years in this role, I have led data modernization initiatives driving impactful decision-making across the organization. My commitment to a bias-agnostic and data-driven approach has led to the development of key performance metrics and innovative features derived from physics models, contributing to resilient risk assessments.I’ve played an instrumental role in elevating SCE\'s public safety and risk mitigation efforts, particularly as a lead data scientist in creating the highly effective Wildfire Risk Reduction Model. This initiative significantly enhances community safety and infrastructure protection. Additionally, I have developed probability of failure models for underground equipment, empowering our public safety strategies and minimizing potential catastrophic failures.My extensive experience includes building end-to-end data pipelines tailored for bespoke transmission asset modeling. These initiatives have profoundly impacted risk quantification within critical projects like the General Rate Case and the Wildfire Mitigation Program, among others. Through strategic analytics and innovative data solutions, my work ensures informed decision-making, effective financial planning, and ongoing success in meeting the challenges of delivering safe, reliable, and clean energy to Southern California.

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