Machine Learning Computer Science

SUYASH SREEKUMAR

+1-541-***-**** San Franciso, USA

******.*********@*****.*** linkedin.com/in/suyashsreekumar github.com/suyash8991 EDUCATION

Master of Science in Computer Science, Oregon State University Sep 2023 – Jun 2025 GPA: 3.96

Relevant Coursework: Deep Learning, Natural Language Processing, Machine Learning Challenges, Principles of System Design, Database Management Systems.

Bachelor of Engineering in Computer Engineering, Mumbai University Aug 2016 – May 2020 GPA: 8.83

EXPERIENCE

Oregon State University Sep 2023 – Jun 2025

Graduate Teaching Assistant Oregon, USA

• Guided 100+ students through lab exercises, SQL query optimization, and database design, boosting project-completion rates and comprehension of data-management concepts.

• Mentored students on advanced DBMS topics and tooling (MySQL, PostgreSQL), reducing common errors by 20%.

• Assisted students in developing proficiency in analyzing and contributing to open-source projects, enhancing their practical coding skills

• Refined lab manuals and course material, clarifying instructions and reducing student queries by 15%. Tata Consultancy Services Aug 2020 – Jul 2023

BI Developer Mumbai, India

• Automated risk-scenario analysis for the Transaction Audit Team with Qlik Sense and SQL, cutting manual effort by 30% and raised audit accuracy.

• Built interactive dashboards for the Management Information and Analytics Team, integrating multi-source data to im- proved decision-making efficiency by 40%.

• Conducted SOX audit testing on key financial processes using Excel and consultation with business heads, ensuring com- pliance and identifying potential risks.

• Designed an executive Power BI dashboard consolidating revenue, cost, headcount, and billing data, reducing manual reporting time by 50%.

PROJECTS

OSU Research Chatbot Oct 2024 – Jun 2025

• Architected a Retrieval-Augmented Generation chatbot indexing 2 000+ PDF chunks; delivered faculty-research answers with <2 s median latency.

• Added metadata filtering and FlashRank cross-encoder reranking, achieving 100% context precision and 0.92 F1 (RAGAS) using Stella-v5 embeddings.

• Built an end-to-end pipeline from web scraping to response generation with structured metadata tagging.

• Evaluated embedding models via the MTEB benchmark, scoring 94% composite.

• Compared three retrieval strategies with RAGAS, reaching 92% F1 and 85% context recall. Adult Income Prediction Jan 2024 – Mar 2025

• Developed a Random Forest classifier with 86% accuracy on the UCI Adult Census dataset.

• Tackled class imbalance (boosting minority-class recall by 9%), NMAR missing values, and outlier detection to enhance robustness.

SKILLS

Data Analysis & Visualization SQL, Python, Excel, Pandas, KPI Reporting, Data Cleaning Programming & Development Python, Java, C,C++ Haskell, Django, Flask, HTML/CSS Tools & Technologies MySQL, PostgreSQL, QlikSense, Power BI, Docker, Kubernetes, Git, AWS, Jira, Jenkins AI / ML LLMs, RAG, FAISS, ChromaDB, Embedding Models, Semantic Search, RAGAS

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