Suhail Mahmud
Senior Risk Modeler Data Scientist @Katrisk
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WORK HISTORY
Senior Risk Modeler Data Scientist @Katrisk
CA, US
Contribute to the development and enhancement of Catastrophe risk models, incorporating the latest scientific research, data, and modeling techniques.Collaborate with cross-functional teams, including product development, engineering, and sales, to integrate Catastrophe risk models into our suite of products and services.Support in the validation and calibration of Catastrophe risk models to ensure alignment with observed data, historical events, and scientific benchmarks, while effectively communicating model uncertainties and limitations.Stay informed about emerging trends, methodologies, and technologies in Catastrophe risk modeling, and contribute to research initiatives to enhance model performance and address evolving client needs.Contribute to the documentation and maintenance of model code, datasets, and analytical methodologies.
EDUCATION
International Islamic University Chittagong
B.sc In Electrical & Electronics Engineering, Electrical and Power Transmission Installers
Motijheel Govt Boys High School
SSC, Science
Rifles Public College
HSC, Science
The University of Texas at El Paso
Master of Science - MS, Computational Science
The University of Texas at El Paso
Doctor of Philosophy (Ph.D.), Computational Science
SKILLS
ABOUT SUHAIL MAHMUD
I am a results-driven Senior Data Scientist with 7+ years of experience building AI-powered risk models across insurance, climate science, and geospatial analytics. I specialize in transforming complex environmental and catastrophe data into production-grade machine learning systems that directly inform loss estimation, portfolio strategy, and climate resilience. Impact Highlights: Improved forecast accuracy by 30%+ through advanced ML and deep learning architecturesIncreased deployment efficiency by 25% by designing scalable MLOps pipelinesLed end-to-end model development using Python, TensorFlow, PyTorch, AWS, and GCP With a PhD in Computational Science and 3+ peer-reviewed publications, I bring deep expertise in operationalizing scalable ML pipelines in real-world environments. My work sits at the intersection of advanced machine learning, atmospheric science, and catastrophe risk modeling — enabling organizations to make data-driven decisions in the face of climate uncertainty. I thrive in environments where scientific rigor meets production engineering, and where models don’t just predict outcomes they drive strategy.
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