Janvi Patel

About Janvi Patel

I turn data into decisions.With 4+ years of experience as a Data Analyst across insurance, finance, and enterprise environments, I help organizations unlock the power of data through predictive analytics, automation, and business intelligence.At MetLife, I work on building automated ETL pipelines, machine learning models for customer churn and risk detection, and interactive Power BI dashboards that support executive strategy and increase business performance. Previously at Cisco and American Honda, I delivered advanced analytics for financial forecasting, portfolio performance, and operational optimization.What I work with every day:• SQL, Python (Pandas, NumPy, Scikit-learn)• Power BI, Tableau, Excel• AWS, Azure Synapse, Snowflake, BigQuery• Predictive Modeling, Forecasting, A/B Testing• ETL, Data Warehousing, AutomationI’m passionate about solving real business problems with data and always open to meaningful connections, analytics discussions, and new opportunities.

Experience

1. Data Analyst

   MetLife

   Mar 2024 — Present

   Automated ETL pipelines using SQL and integrated with Snowflake, reducing manual ingestion of policy, claims, and customer data by 50% and enabling near real-time analytics for underwriting and claims management teams.• Implemented data transformation logic with Python (Pandas, NumPy) and SQL for policyholder deduplication, claims ranking, anomaly detection, and fraud checks, improving data accuracy and integrity by 30%.• Built predictive ML models using Scikit-learn on AWS SageMaker, analyzing policy lapse risks and customer churn patterns, leading to a 20% increase in customer retention through data-driven renewal strategies.• Developed interactive Power BI dashboards covering churn, claims performance, revenue forecasts, and fraud detection, improving forecasting accuracy by 30% and enabling actuarial and business leaders to make data- driven decisions.• Optimized cloud-based data architecture by integrating AWS S3, AWS Lambda, and Redshift, reducing claims query latency by 40% and ensuring scalable, high-performance data processing across policy administration systems.• Conducted exploratory data analysis (EDA) using Python, SQL, and Tableau, uncovering key trends in claim ratios, customer demographics, and policy profitability to support strategic decision-making. • Partnered with cross-functional teams (actuarial, product, and operations) in Agile/Scrum environment, translating business requirements into actionable insights, improving reporting efficiency, and streamlining decision workflows.

Education

• CHARUSAT

  BCA, Computer Science

  2017 — 2019