Jun 2024 – Mar 2025 · 9 mos · Tempe, Arizona, United States · Remote

• Developed and deployed interactive dashboards using Power BI, and custom-built ETL scripts, reducing reporting time by 50%.
• Redesigned and optimized legacy data pipelines, resulting in a 25% reduction in processing time by streamlining ETL processes and eliminating redundant steps.
• Implemented a normalized schema and efficient query structures in the SQL relational database, improving data retrieval speed by 30% by reducing the number of joins required and optimizing query execution plans.
• Developed and deployed 2+ machine learning models (e.g., Random Forest, SVM) that improved
• business forecasting accuracy by up to 15%, leveraging GCP services for real-time analytics.

Apr 2021 – Jul 2022 · 1 yr 3 mos · Bengaluru, Karnataka, India · Hybrid

• Improved customer service efficiency by 15%, achieved through Implementing a Scalable ETL Batch Data
• processing pipeline for CSAT dashboard, improvement was measured by pre- and post-implementation
• CSAT scores and response time metrics. Utilized Python, SQL queries and tools like AWS Glue, PostgreSQL,
• EC2, Code Commit and Apache Airflow.
• Significantly reduced dashboard data loading latency by 50%, cutting average load times from 10
• minutes to 5 minutes, by implementing an ELT pipeline using AWS Glue, and Airflow to designing a
• DataMart optimized with Python/PySpark and SQL queries.
• Boosted employee retention by 15% through a predictive churn model. Integrated a Random Forest
• model into sales operations to proactively identify at-risk employees, enabling targeted retention
• strategies.
• Collaborated with cross-functional teams to design & implement an optimized data processing workflows
• analysis using Python API scripts for automated scheduling, resulting in 50% faster and more accurate
• sales reporting.

Jun 2019 – Mar 2021 · 1 yr 9 mos · Bengaluru, Karnataka, India

• Achieved 85% accuracy in inventory and sales forecasting by developing and deploying modules using ARIMA, ARMA, Random Forest, and Support Vector Machine models. Leveraged GCP services like VM, BigQuery, Cloud Storage, PostgreSQL and Apache Airflow.
• Improved model performance and prediction accuracy by 10% through pre-processing techniques such as feature engineering and dimensionality reduction.
• Achieved 83% accuracy in real-time aircraft detection at 30 frames per second by developing an end-to-end object detection application using the Regional CNN model. Utilized TensorFlow, Code Commit, and Python Flask.
• Implemented information technology solutions for machine learning papers on forecasting and object detection, presenting demos to senior team members and clients.