- Built a high-performing engineering team of 24 from the ground up, strategically identifying andrecruiting top talent while maintaining high retention and long-term performance.- Pioneered the implementation of generative technology within AppleCare. Architected solution and led team to build an entire end-to-end generative system that first went into production March 2022.- Directed multiple high impact cross-functional machine learning projects. This involved guidingengineers, collaborating with project stakeholders, architecting solutions, and driving new productfeatures for our conversational AI platform to ensure a quality product is delivered.

- Drive machine learning solutions forward by creating models which utilize the latest research in NLP. Hired and continue to manage team of 9 analysts, engineers, researchers, and product people.- Spearheaded complete machine learning geo expansion strategy. Conceptualized and gained consensus for set of product features which cut resource cost of model expansion by 30% and required effort by 80%. Impact on model performance in initial tests show accuracy reduction of only 7% on average compared to traditional approach. - Designed and built data annotation/maintenance tools powered by machine learning. These tools decreased the total effort for modifying existing training sets by ~20%. Improved data quality a key driver for 12.5% accuracy increase mentioned earlier. - Head multiple high impact machine learning projects. This involves guiding engineers, collaborating with project stakeholders, mentoring junior team members, and driving new product features to ensure a quality product is delivered.- Elevate machine learning community across all of Apple. Key organizer for internal machine learning conferences, websites, and tools. Lead multiple events with hundreds of internal attendees.

- Led effort to increase coverage of team’s core machine learning product by 9% and classification accuracy by 12.5% through various application level and model improvements.- Cut training time of a production deep learning model by ~90% through rethinking/rewriting entire machine learning pipeline and model training process. Simultaneously increased accuracy of model by 10%.

- Search, anomaly detection, NLP, and ML ops.

Utilized data mining, Markov chain, and supervised learning techniques to glean insights from new and previously unexplored data at Apple in order to develop a fraud detection system. Responsible for the entire pipeline including data collection, data wrangling, model training, and model deployment.

Developed key features for virus visualization and containment tool as a member of the data science team. Implemented the first instance of Apache Cassandra for the Data Services group. Completed dashboarding solutions to provide valuable insights in Oracle database performance and health.

Developed a Node.js application to test Buttercoin’s services extensively and provided bug fixes as necessary.

Ranked a top 5 intern in the GITS department.