Data Scientist with 2+ years of experience in Insurance and Financial Services, specializing in customer analytics, propensity modeling, fraud detection, predictive analytics, and Generative AI applications. Experienced in developing and deploying machine learning solutions using Python, PySpark, Databricks, AWS, and Azure.
I work on building data-driven solutions that help businesses make better decisions through machine learning, analytics, and AI. My experience spans customer analytics, underwriting, claims analytics, fraud detection, customer propensity modeling, and Generative AI applications.
- Machine Learning
- Customer Analytics
- Propensity Modeling
- Fraud Detection
- Predictive Analytics
- Generative AI
- Natural Language Processing
- Risk Analytics
- Data Engineering
- Python
- SQL
- PySpark
- Classification
- Regression
- Clustering
- Feature Engineering
- Model Evaluation
- XGBoost
- Random Forest
- Decision Trees
- Large Language Models (LLMs)
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- OpenAI API Integration
- Document Understanding
- AWS
- Azure
- Databricks
- Azure Functions
- Azure Storage Accounts
- Power BI
- Tableau
Developed and deployed customer propensity models across multiple financial products to support customer engagement and cross-sell initiatives.
Key use cases:
- Intraday Trading Activation
- Mutual Fund Cross-Sell
- Customer Segmentation
- Behavioral Analytics
Responsibilities:
- Data preparation and feature engineering
- Model development and validation
- Production deployment
- Performance monitoring
- Stakeholder collaboration
Developed machine learning solutions to identify suspicious customer and transaction patterns.
Responsibilities:
- Feature engineering
- Classification modeling
- Risk signal identification
- Model optimization
Built predictive models to estimate claim outcomes and support operational decision-making.
Responsibilities:
- Data preprocessing
- Predictive modeling
- Model evaluation
- Business insight generation
Contributed to the digitization of health underwriting workflows through automated extraction and processing of medical information.
Developing AI-powered solutions that simplify insurance policy documents into customer-friendly explanations using Large Language Models.
Responsibilities:
- Prompt engineering
- LLM integration
- Document understanding
- Workflow automation
- Generative AI
- Large Language Models
- Natural Language Processing
- Financial AI
- Risk Modeling
- Applied Machine Learning
- AI Research
- Production-grade AI systems
- Retrieval-Augmented Generation
- LLM Fine-Tuning
- Financial Forecasting
- Responsible AI
Currently exploring advanced topics in:
- Generative AI
- Financial Forecasting
- MLOps
- Deep Learning
- AI Research
GitHub: https://github.com/jatin837
LinkedIn: https://www.linkedin.com/in/jatin837/