Ravi Joon
Data Scientist | Pramerica Life Insurance | Machine Learning, AI & Deep Learning | Driving Business Impact with Data
- Role
- Associate Data Scientist at Pramerica Life Insurance
- Location
- Gurugram, HR, IN
- LinkedIn followers
- 500 followers
About Ravi Joon
Ravi Joon is a Data Scientist with 2.5+ years of experience in designing and deploying end-to-end AI and machine learning solutions. At Pramerica Life Insurance, he has worked on projects spanning predictive modeling, customer analytics, and AI-powered chatbots that enhance decision-making and improve business outcomes.With a strong foundation in Python, SQL, machine learning, deep learning, and data visualization, Ravi specializes in transforming complex data into actionable insights. He is passionate about leveraging AI/ML to solve real-world problems, optimize processes, and deliver measurable value.Driven by curiosity and continuous learning, Ravi actively explores advancements in generative AI, transformers, and modern analytics frameworks to stay ahead in the rapidly evolving AI landscape.
Experience
Associate Data Scientist
Sep 2023 — Present · Gurugram, IN
Driving data-driven decision-making and AI solutions across underwriting, customer retention, and internal knowledge management:High-Risk Underwriting Model: Designed and deployed an XGBoost-based ML pipeline to identify high-risk applicants. Achieved precision 82%, recall 87%, F1-score 84%, and AUC-ROC >0.90. Implemented PSI/CSI monitoring for model drift.Propensity-to-Pay Model (Renewals): Developed predictive models using XGBoost to prioritize renewal outreach. Achieved F1-score 84% and boosted collections.RAG-based Employee Chatbot: Built an intranet chatbot using LangChain, FAISS, Gemini 2.5 Flash, and MongoDB, reducing employee query resolution time.Field Agent Conversational System: Created multi-agent AI tools for sales pitch generation and policy lookups, achieving ~92% retrieval accuracy and enhancing agent productivity.Insurance Information Bureau (IIB) Data Analysis: Analyzed records, benchmarking internal performance vs. industry, informing P&L decisions on product persistency and sales penetration.BAU Analytics & Model Monitoring: Conducted ECDR analysis, managed scoring runs, and ensured continuous monitoring for deployed ML models.Tech Stack: Python, SQL, XGBoost, Scikit-learn, Pandas, NumPy, Power BI, LangChain, FAISS, MongoDB, Gemini 2.5 Flash, Google Embeddings, Excel
Education
Great Lakes Institute of Management
Post Graduate Program in Data Science Engineering
2022 — 2023
Dronacharya College of Engineering
Bachelor of Technology - BTech
2015 — 2019
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