Lead data team in data operations and management, develop scalable genetic analysis pipelines, apply machine learning and deep learning approaches in gene-based Alzheimer's Disease analyses. Actively work with PyTorch, Hail-PySpark, Amazon RDS databases, and EC2 based containerized deployments.

In the Biomedical Genetics Section of Department of Medicine, leading the development of data pipelines and Machine Learning solutions on leading Alzheimer's Disease (AD) projects including AI4AD (https://twitter.com/ai4ad).

Worked on herd analytics and visualizations of student behavior on virtual lab platform to derive insights on student learning using techniques like conformity and sequence analyses.

Played a leading role as Data Scientist to plan and develop Machine Learning and Deep Learning models that helped improve threat detection systems (Security CT) and addressed bottlenecks. This was achieved through:- Identifying gaps in the exiting process and addressing those using data-driven techniques- Proposing solutions and plans of action for development- Research and development of Computer Vision based Deep Learning models like Faster Region based Convolutional Neural Networks (FasterRCNN), Capsule Networks, etc. using tools like the MXNet and Keras/TensorFlow framework- Product evangelization by enabling quick adoption through easy-to-use packaged implementation and documentationHandled the project end-to-end that formed the basis of my project thesis. The product significantly reduced process bottleneck and led to significant savings in operations and development.

In a lead role as Data Scientist, led multiple teams in development of models, dashboards, backend services and data pipelines in major client facing accounts.- Spearheaded the development of Deep Learning based Entity Resolution model for a major insurance client in better understanding its customers- Developed full-stack (MEAN) web-app dashboard for clients using tools like D3.js, Google Charts- Created prototypes as part of innovation and development team through the application of Machine Learning and Deep Learning models in use-cases like intelligent reporting and root-cause analyses- Mentored team members in best development practices

Worked in Data Analyst and Data Engineering and Software Dev roles with multiple industry verticals in client facing environment. These included - - Data analyses using statistical techniques/ML/Excel-VBA/Pivot Charts- Developing quick parsing of raw data for near real-time data pipelines- Database management (relational SQL, NoSQL)- Big data processing using Hive and HDFSThe role involved developing data solutions for clients using problem breakdown approaches.