During my time at Accel Robotics, I played a critical role in implementing cashier-less retail stores through cutting-edge technologies in machine learning and computer vision. My expertise helped develop object detection and real-time entity tracking solutions. In addition, I created a custom video object-tracking labeling tool that significantly improved our data collection and labeling process. I also pioneered the ultra-wideband system, which was crucial in tracking customer and employee movements within the store.My dedication to ensuring the system's smooth operation led me to monitor and maintain the data collection, preprocessing, and experiment tracking pipelines.Overall, my experience at Accel Robotics allowed me to develop my MLOps skills further and provided me with a comprehensive understanding of the challenges and opportunities in retail technology.

As a Product Manager at Teradata, I led the development and delivery of AI Consulting Services, leveraging my experience in data science to drive customer success. I was responsible for scoping projects, conducting customer interviews, and prioritizing and designing prototypes. In addition, I created sprint plans to achieve long-term goals and continuously improved our offerings through customer feedback and asset development. My responsibilities also included presenting service roadmaps and business plans and writing presentations, proposals, and customer-facing materials.

At Teradata, I leveraged my expertise as a Data Scientist in the Emerging Practices Group to collaborate on several impactful projects. One of my critical projects involved working with a major U.S. bank to detect and prevent fraudulent activity. I traveled to customer sites and collaborated with teams to deliver a robust solution. Building upon this experience, I was also involved in streamlining data preprocessing. I helped create a user-friendly tool using Jupyter notebooks and widgets that other data scientists could utilize. Another project I worked on involved developing a recommender system for a leading retail store. The goal was to provide the best offers to customers, and I utilized off-policy reinforcement learning and contextual bandits to create a proof-of-concept solution. In addition to these projects, I applied model-based reinforcement learning techniques to scheduling problems. The results were comparable to traditional operations research tools in effectiveness.

At the Cottrell Lab, I was crucial in advancing the current method for detecting parasites in microscopy images. In addition, I developed a sophisticated cell and parasite detection system using Convolutional Neural Networks. My work played a crucial role in the lab's mission to enhance the speed of drug discovery. As a solo contributor, I took charge of data preprocessing and modeling while collaborating with another research group for data collection and analysis.