Experiences
Apr 2023 - Present
Rivian
Sr. Data Scientist
Architected an Agentic AI layer and RAG-based systems to automate complex lifecycles for 2,000+ users, effectively reducing IT support volume by 20%. By engineering multi-step reasoning workflows and LangGraph agents on Databricks, manual change-management and reconciliation efforts were reduced by 23 hours per week. Further operational gains were achieved through the integration of Generative AI developer tools, which accelerated deployment cycles by 3 months, and the implementation of predictive ML models to minimize downtime. These efforts culminated in the development of a unified autonomous ecosystem featuring a sophisticated AI Memory framework to manage stateful reasoning across mobile, web, and vehicle surfaces.
Data Scientist
Enhanced allocation of shared line-side inventory using advanced ML classification models (XGBoost, LightGBM), boosting utilization by 20% and ensuring stock distribution matching to teams’ requirements. Implemented a Reinforcement Learning (Q-Learning) system to autonomously optimize inventory and production planning – a project recognized with a Leadership Innovation Award. Streamlined data from multiple systems (SAP & MES) into cloud platforms (AWS Redshift, Snowflake, Databricks) and created executive-level Tableau dashboards, improving visibility and decision-making.
Aug 2022 - May 2023
IU Marketing Analytics
Data Scientist
Implemented advanced Alteryx ETL model automations to enhance API connector performance, reducing data processing time and enabling seamless integration into Big Query. This initiative saved 10 man-hours per week and streamlined workflow efficiency. Additionally, optimized pipeline development strategies within marketing funnels by leveraging Big Data tools like Hive and Spark, leading to a significant 15% reduction in data maintenance costs. Furthermore, implemented GA4 processes to track user behavior and improve marketing effectiveness. By translating these insights into Looker Studio visuals, decision-making was facilitated, resulting in a notable 2% increase in website conversions.
Aug 2021 - Apr 2022
Indiana University
Research Assistant
Pioneering a data-driven approach, spearheaded the integration of state-of-the-art Ml & DL techniques into our research framework. This involved the development and deployment of ML and DL classification models, alongside neural network architectures, to accurately determine the age of real-world images with an precision rate of 78.3%. Additionally, leveraging NLP algorithms such as FastText, developed a failure mode prediction systems, resulting in a substantial 53% enhancement in quality metrics. Moreover, by employing NLP techniques for feature engineering, extracted valuable insights from textual data linked to images.This modified our models with crucial features, significantly improving their predictive capabilities.
Jun 2022 - Aug 2022
EMC Insurance Group
Data Scientist
By implementing regression models such as Random Forest, Gradient Boost, and XGBoost, alongside meticulous feature engineering and optimization of hyperparameters on AWS and Snowflake data, I achieved an impressive 60.1% damage prediction rate. Delving into extensive research and deep analysis of large datasets, I identified crucial trends and integrated BERT, leading to a notable 10% improvement in the accuracy of insurance analysis. Moreover, through the development of interactive dashboards and visualizations using Power BI, stakeholders gained enhanced insights, aiding in the identification and rectification of faulty investments totaling $260k.
Dec 2019 - Jul 2021
Deloitte
Business Intelligence Analyst
Managed the end-to-end development of an Oracle Cloud RDBMS framework and the implementation of data analysis procedures, ensuring a seamless migration process. Through the strategic use of MySQL, Python, and Bash scripts, data processes were optimized to reduce migration timelines by 3%. By applying advanced statistical methods to data trend analysis, an accuracy rate exceeding 85% was achieved, delivering critical insights for strategic decision-making. Furthermore, project efficiency was bolstered by establishing a CI/CD pipeline for data warehouse migrations, which automated the testing and deployment of SQL scripts. This integration—leveraging Git for version control and automated data validation—effectively reduced manual oversight by 10 hours per week.
Dec 2019 - Dec 2019
Amazon
Data Scientist
Played a pivotal role in developing an Oracle Cloud RDBMS framework and implementing data analysis procedures, resulting in a seamless migration .Also by leveraging MySQL, Python, and Bash scripts, optimized data processes, ultimately reducing migration time by 3%.Utilizing statistical methods conducted data trend analysis with an impressive accuracy rate of over 85%, providing valuable insights for strategic decision-making.
Things I've Built
Education
Indiana University Purdue University Indianapolis
Masters, Applied Data Science
GPA 3.8
Courses: Database Management, Programming - Data Science, Cloud computing, Web Database development, Data Analytics with R, Machine Learning, Deep Learning & AI, Statistical Learning and Data visualization.
Jawaharlal Nehru Technological University
Bachelors, Electronics Engineering
GPA 4.0
Courses: C, Java ,Matlab, Python, Database management,Data processing at scale, Advanced computer networks,Operating systems
Skills
