Data Scientist at Scry AI

Working with Python, Docker, Kubernetes, and AWS technologies in a team of 10 people

Creating and integrating new modules and functionalities into an existing product to improve and enhance it further.

Created and integrated new modules and functionalities into an existing product to improve and enhance it further.

Developed proofs-of-concept and demos for clients according to their requirements.

Researched and explored the latest research advancements to implement and integrate them into the current product to improve the current state-of-the-art version.

Assisted the professor in teaching, grading, and proctoring a class of 300+ students in a graduate-level course with a team of 16 teaching assistants and course producers.

Created programming assignments and Exams, and held weekly office hours for students to guide and clear their doubts.

Serving as the first line of support for customer technology to all faculty, staff, and students when using general-use classroom audio-visual equipment in any of the auditoria, classrooms, collaboration spaces, and computer labs across campus. 

Guided and mentored fellow mentees in the following fields: Interviewing for a Job, Programming, Data Science/Computer Science Tools and Technologies

Scheduled virtual mock interviews and sessions for explaining programming concepts

Developed Spark - Scala modules to collect, transform, handle, and store about 300TB of client’s data from legacy on-premise systems like Business Objects Data Services SAP (BODS), IBM Integration Hub (IIB) and Oracle Golden Gate (OGG) on HDFS and AWS Cloud, enabling the client to analyze on the complete data and statistics to make business decisions

Created 2 new project opportunities from client worth $50,000 by training 10 junior employees in Python and PySpark technology

Increased the efficiency of the project and saved 20% time of the team by automating key repetitive tasks with Unix shell scripting 

Reduced the cost of hiring temporary staff by an estimated 10% annually by developing Machine Learning and time-series models in Python on the client’s 5-year data to predict the number of children expected to attend E-Day-Care facilities located in California, USA