Aug 2025 – Present · 9 mos

• Authored articles and tutorials on Data Science and Machine Learning
• Speaker on DataHour

Jan 2025 – Present · 1 yr 4 mos

• Member of IEEE Standards P3579 - Standard for Artificial Intelligence Applied to Time Series
• Reviewer at IEEE TNNLS (Top 10 AI Journal), IEEE TFS (Top 10 AI Journal), IEEE IOT
• Senior IEEE Panel Member, 2025
• Contributor for IEEE Inisght

Jun 2023 – Present · 2 yrs 11 mos · New Jersey, United States · Hybrid

• Developing agents and predictive models to improve product selection and availability on Walmart.

Nov 2022 – Jun 2023 · 7 mos · Arlington, Virginia, United States

• Lead efforts in recommender system for new hire content recommendation, information extraction from free-text to automate thematic analysis, creation of text embedding to help predict employee career outcomes

Jan 2021 – Oct 2022 · 1 yr 9 mos · Arlington, Virginia, United States

• Lead development of large-scale parallelized feature selection, data pre-processing, XAI and monitoring systems to help improve efficiency and interpretability of downstream career outcome prediction models.
• Developed production-grade AWS based platform (S3, Glue, ECR, ECS, SageMaker Pipelines) for serving online and batch machine learning solutions built for FinTech/Accounting teams within Amazon

May 2020 – Aug 2020 · 3 mos · Seattle, Washington, United States

• Experimented with ETS, ARIMA, DeepAR, and Prophet for forecasting transactional volumes of Ring products to allow the business to proactively work with payment vendors. Achieved MAPE of 9% with the SARIMA model
• Developed an ETL Python pipeline hosted on AWS Lambda, S3, and DyanmoDB to automate the month-end close process for Accounting Team and created savings of 213 manual hours annually
• Secured 1st Place in the Machine Learning Academy, Natural Language Processing competition, 2020 for "Product Safety Classification"
• Condcuted a three day session on Machine Learning for Natural Language Processing for 40+ Amazonians via the Machine Learning Academy.

Jun 2022 – Dec 2022 · 6 mos

• Authored articles and tutorials on Data Science and Machine Learning

Aug 2020 – Dec 2020 · 4 mos · Pittsburgh, Pennsylvania, United States

Mar 2020 – May 2020 · 2 mos

Dec 2019 – May 2020 · 5 mos

• Technology for Effective and Efficient Learning (TEEL) Lab at CMU focuses on research in learning methods, where I worked on curriculum development and automated testing for data exploration and machine learning basics

Jul 2017 – Jul 2019 · 2 yrs

• Promoted to architect and lead the development of minimum viable data science solutions across Dell Order Experience teams on the Dell Data Reservoir (140 node Hadoop Cluster)
• Mapped application logs to a Finite State Machine and used Bayesian networks to identify root-cause (accuracy ~85%) of application errors and recommend solutions thereby reducing issue resolution time by 50% (Patent Approved)
• Collaborated with delivery, sales, and analytics divisions to develop a multivariate linear regression model that predicted the lead time for stock items thus improving the accuracy of Estimated Delivery Date (EDD) by ~37%
• Implemented an ensemble model (SVM, XGBoost, and Logistic Regression) to predict (accuracy ~92%) and proactively act on issues in order processing thereby improving customer experience (Patent Approved)

Jul 2015 – Jun 2017 · 1 yr 11 mos

• Built a 6-node Hadoop Cluster to analyze and visualize near real-time data on Tableau that allowed to obtain actionable insights on bottlenecks that reduced turn-around time on issues by 33%
• Led the development of an alerting framework based on statistical process control rules (accuracy~85%) to proactively identify processing anomalies and prioritize them based on business impact
• Implemented a multivariate linear regression model (accuracy ~90%) to predict the time an order should spend in a processing stage and helped prioritize orders to be resolved based on the perceived business impact of delay

May 2014 – Jul 2014 · 2 mos · Bangalore

• Built a parser and sentiment analyzer (accuracy ~78%) to extract consumer comments from Dell Twitter Feed and identify the sentiment towards Dell products and the delivery experience
• Developed an interactive dashboard to analyze consumer opinions and enabled senior executives gauge real-time sentiment during the release of bitcoin as a payment option on Dell.com (2014)

Jul 2012 – Apr 2015 · 2 yrs 9 mos · Calicut

• Developed website for FOSSMeet 2012, the fourth largest open-source get together in India.
• Developed and maintained websites for Computer Science and Engineering Department (CSE Alumni Website, CSEA Website).
• Part of selecting and mentoring junior executives of CSEA (fields include Web design and Development, competitive coding)