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Data Analyst with 1+ years in Data Visualization & Machine Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Analyst with hands-on experience in dashboards, KPI reporting, and data pipelines using Python, R, SQL, Tableau, Power BI, and Advanced Excel. Skilled in translating complex data into actionable insights for cross-functional stakeholders.
University at Albany, SUNY
Masters · Data Science
August 1, 2024 – May 1, 2026
University of Mumbai
Bachelor of Engineering · IT
August 1, 2019 – May 1, 2023
New York State Department of Health
Student Assistant - Data Analytics Reporting
May 1, 2025 – May 1, 2026
New York City, New York, United States
CodeClause Pvt Ltd
Data Science Intern
June 1, 2024 – July 1, 2024
Mumbai, Maharashtra, India
Cognifyz Technologies
Machine Learning Intern
May 1, 2024 – June 1, 2024
Mumbai, Maharashtra, India
Transformer Grokking - Generalization Beyond Memorization
April 1, 2026 – May 1, 2026
Built a GPT transformer from scratch in PyTorch to reproduce a published paper on "grokking", where a model generalizes long after memorizing data. Ran 24 experiments across model sizes, primes, and seeds, all achieving above 99.4% test accuracy on modular arithmetic tasks. Found through ablation testing that weight decay is essential for grokking and increasing it speeds up generalization by ~2,400 training steps.
Intruder Detection System
February 1, 2026 – March 1, 2026
Built an end-to-end ML model to detect a specific user across 336,000 web sessions (<1% positive class); engineered a Bag-of-Sites sparse matrix compressing memory from 130 GB to 22 MB. Applied TF-IDF site weighting and time-based features (session hour, morning/weekend flags), boosting AUC by +0.04; tuned regularization (C = 0.036) via grid search. Designed a custom 3-fold time-based CV scheme reducing evaluation variance from ±0.09 to ±0.0018, final ROC-AUC: 0.920 to 0.963.
Data-Driven Time Series Analysis of NYS TAP Financial Aid
January 1, 2026 – May 1, 2026
Processed 270K+ records in R; engineered normalized metrics (aid per student, aid per FTE) for cross-sector benchmarking across New York State institutions. Applied nonparametric statistical tests to evaluate financial aid distribution shifts over time. Found that TAP funding changes are driven by increases in aid amounts not enrollment growth providing actionable insight for resource allocation strategy
Cultural Fit Analysis
The candidate's academic background and project diversity, ranging from deep learning research to practical ML applications and financial aid analysis, indicate a strong curiosity and willingness to tackle varied challenges. Their experience in both academic and government settings, along with internships, suggests adaptability. The focus on public health data and financial aid analysis aligns with roles that require a strong sense of responsibility and impact, which is a positive indicator for cultural fit in mission-driven organizations.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical and problem-solving skills through their project work, particularly in optimizing data processing and model performance. Their experience in coordinating cross-functionally and presenting complex data to stakeholders indicates good communication and collaboration potential. The role at NYS Department of Health highlights an ability to work in a structured environment and contribute to policy-level decisions, suggesting a good operational fit for roles requiring data-driven decision support.