Summary
Jayant Apte is a Principal Core Data Scientist with 12 years of experience building ML-driven solutions for life and property & casualty insurance, specializing in turning research-grade models into production-grade underwriting products. He has led integration of survival ranking and binary classification models that directly unlocked several million dollars in additional annual premium for clients, and he pairs technical depth in XGBoost Cox models with practical experiment tracking, model versioning, and production support. Comfortable collaborating with medical underwriters and cross-functional teams, he uses SHAP and dependence analysis to make opaque models actionable for domain experts. Jayant also drives analytics tooling and decision-support dashboards—such as anomalous portfolio detection and vendor-data valuation—that address high-impact business pain points. He mentors junior talent, runs organization-wide trainings on generative AI and SOLID Python design, and has guided interns into full-time roles and publishable research. Based in Huntersville, NC, he combines a PhD-level engineering background with hands-on applied research in fairness, knowledge distillation, and survival analysis to deliver measurable value in regulated industries.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Electrical Engineering, Doctor of Philosophy (Ph.D.), Electrical Engineering at Drexel University
Bachelor of Engineering (B.E.), Electronics and Telecommunication Engineering, Bachelor of Engineering (B.E.), Electronics and Telecommunication Engineering at Vidyalankar Institute of Technology
Marathi, Hindi