Education &
Experience

Master of Science in Data Science

• Coursework: Machine Learning, Recommender Systems, Statistical Modeling, Big Data Analytics, Scalable Data Systems, Data Systems for ML
• Part-time Student Build & Release Engineer at UCSD IT Services
• DataHacks 2026: 2nd Place Cloud Track + Best Use of Snowflake Cortex Award for GridGreen

Bachelor of Technology - Computer Science

• Specialisation in Data Science & Big Data Analytics
• Coursework: DBMS, Algorithm Design, Data Visualisation, Data Warehousing & Mining
• Best Project of the Cohort for Take A Trip
• Top 30 nationally, Codehers Coding Challenge 2022

High School (11th & 12th) - Maths, Physics, Chemistry

• CGPA 9.75/10

Schooling

• CGPA 10/10

• AWS Academy Cloud Foundations
• Big Data Analytics - UCSD / Coursera
• IBM Data Science Foundations Level 1 & 2

• 2nd Place, Cloud Track + Best Use of Snowflake Cortex at DataHacks 2026, a 36-hour hackathon at UCSD, for GridGreen
• Star of the Month at IBM, Nov 2023 - consistent contributions across ETL, SQL, and analytics
• People's Choice Award at IBM - multi-cloud provisioner project, 40% cloud cost reduction
• Top 30 nationally in the Codehers Coding Challenge, 2022
• Best Project of the Cohort, KL University - Take A Trip

Student Build & Release Engineer (Part-Time)

• Keeps CI/CD running for campus engineering services: catches broken builds early, automates release checks, and writes deployment docs so the next person does not have to reverse-engineer what happened.

Software Developer (promoted from Intern)

• Worked on WatsonX AI evaluation: wrote test prompts, reviewed output quality for correctness and consistency, and documented what the model kept getting wrong so the product team had something concrete to act on.
• Rebuilt a data validation pipeline that was silently failing and taking down batch jobs with it. Understood how engineers actually used it first, then restructured around those workflows. Processing time dropped 90%.
• Built an LLM-based router that analysed code diffs and decided which cloud environments actually needed testing. Wrote custom metrics to evaluate its decisions. Cut redundant CI/CD runs and saved 40% in compute costs.
• Ran clustering and time-series analysis on multi-cloud usage logs to find waste patterns. Wrote findings in plain language so they would land with stakeholders not looking at raw data.
• Contributed to multi-cloud provisioning tooling: built backend services using Python and IBM Cloud APIs to automate infrastructure setup across AWS, Azure, and IBM Cloud.
• Star of the Month, Nov 2023 - ETL pipeline rebuilds, SQL warehouse optimisation, and analytical reports adopted by Finance and Engineering teams.
• People's Choice Award - multi-cloud provisioner that standardised usage data across AWS, Azure, GCP and reduced cloud costs by 40%.

Research Intern, NLP & Speech

• Built a pipeline to generate phoneme data from 10,000+ contact names using the CMU Pronouncing Dictionary. Ran accuracy checks against ground truth; the 10% gain in recognition fed directly into the next model iteration.
• Worked closely with research leads to define what "good enough" looked like for the model, surface edge cases that automated checks missed, and communicate findings in a way that shaped follow-up decisions.

Cloud Intern

• Completed cloud computing certification via AICTE and deployed a website on AWS EC2 with VPC isolation and Apache HTTP server.