Client: Google & FactSet Research Systems Inc.1. Developed a system for extracting financial details from user-provided data using Fast API & GPT-4o through dynamic fewshot prompting, improving upon the previous regex-based method. The extracted information is then passed to a secondary API that queries a PostgreSQL database for further details, streamlining the process and providing users with comprehensive financial insights through the BOAT application.2. Build, deploy, and tune machine learning models, and maintain ML pipelines to enable real-time data analytics and support data-driven decisions.3. Implemented a multiclass model for ticket management, categorizing tickets into Product, Policy, Process, and Other. This reduced ticket resolution times and improved efficiency, leading to a 17% increase in overall resolution speed.4. Built and managed dashboards for Google Play Store apps, enabling enforcement actions and monitoring app performance. Additionally, developed team-specific dashboards using Google PLX.5. Optimized legacy SQL code for Google Dremel/F1 in BigQuery to enhance query performance.

Serve as mentor to a fellow going through the 1yrs intensive PGP program in Management (Analytics specialization), Machine Learning/Data Science/Business Analytics/Business Intelligence & for B2B Analytics Programs.

1. Collaborated closely with leadership, including the CEO, as well as cross-functional teams (Tech, Product, and Sales) to drive product marketing, sales enablement, and outbound marketing automation.2. Developed ML models to assess customer buying propensity based on behaviors; leads with a propensity above 25% were categorized as quality leads for CRM integration.3. Delivered weekly and monthly reports to Customer Service, Sales, Finance, and Product Team, including forecasts for support ticket inflow and performance metrics.4. Built a scalable ETL pipeline with Airflow and AWS Lambda to retrieve and clean weekly API data, stored in S3, transformed with AWS Glue Spark, and loaded into Snowflake via Snowpipe for reporting and ML analysis.5. Developed time series models, including ARIMA, SARIMA, and Prophet, for forecasting warehouse stock and sales, improving inventory planning and sales strategy.6. Performed customer segmentation using KMeans clustering, identifying optimal timings for sales agents to target new customers and schedule follow-ups, improving outreach effectiveness.7. Analyzed purchasing behavior patterns, such as separate purchases of notes and top-end products, to develop a combo package strategy. Validated the approach through AB testing, leading to a significant increase in sales performance and contributing 15% increase in annual revenue.8. Mentored security team agents in transitioning from Excel to Tableau, enhancing their ability to build, publish, and maintain dashboards, which significantly improved reporting efficiency and data visualization quality.

1. Managed client data migration from various Online Booking Tools (OBTs) to a new OBT, ensuring a smooth transition and minimal disruption.2. Tested and validated data conversions to guarantee accuracy and successful project completion.3. Configured Global Distribution System (GDS) profile data into GBT’s database and tools, enhancing system integration.4. Use SQL/NOSQL queries in SOLR for enterprise search and analytics5. Created comprehensive reports and visualizations using Tableau and Excel, providing actionable insights for business decision-making.

1. Managed customer queries and support related to flight bookings, including confirmations, re-issuance, refunds, and PNR updates.2. Managed ticket stock inventory and ordering processes to maintain optimal stock levels and prevent shortages.3. Analyzed and consolidated customer booking data, producing detailed reports with Tableau, Power BI, and Excel, which informed strategic decision-making and operational improvements.