Hey, I'm
$ git log --oneline --graph
Five years of turning messy data into statistical evidence set the direction. Every stop since has been about building models and systems that hold up under real scrutiny, not just look good in a notebook.
feat: SQL-based data validation framework, -60% discrepancies
First real lesson in production data: a 4.5M-customer billing base doesn't forgive weak validation. Designed row-level checks and reconciliation logic that cut discrepancies 60% and shortened month-end close by 10%.
feat: anomaly detection at scale, 600K+ subscribers
Moved from fixing issues after the fact to designing the checks that catch them first — anomaly detection and reconciliation holding 98%+ accuracy within SLA. Earned "Best Upcoming Talent," FY23 Q3.
feat: forecasting + agentic auditing, 92% accuracy
Went back to build the theory behind the practice: statistical forecasting models and a first prototype of autonomous agents doing continuous compliance auditing.
feat: AI cost-attribution analysis, 5.5x growth surfaced
Where the two threads met: quantitative analysis of AI agent costs inside shared compute, and a 211-rule data quality framework shipped to a $159B production pipeline.
$ cat PRINCIPLES.md
Everything I build is grounded in a production mindset.
"Understanding existing systems deeply before changing them, designing for the next person who has to maintain my work, and communicating technical findings in a way non-technical stakeholders can act on." That’s the standard I actually build to.
AI Sentinel pairs its local-LLM explanation layer with a deterministic rule engine and a 100%-deterministic fallback, so a hallucinated inference never becomes an unchecked compliance decision. Every finding is SHA-256 audit-logged, nothing is a black box.
→ see it in AI Sentinel 02The Resume–JD matching pipeline didn't jump straight to neural re-ranking. It proved out a TF-IDF baseline first, then SBERT embeddings, then supervised refinement, each stage measured against the one before it, so every added layer of complexity earned its place.
→ see it in Resume–JD Matching 03SBERT's embeddings accelerate semantic matching far beyond what keyword search alone could do, but every embedding-based match is still validated by the supervised neural layer against labeled outcomes before it becomes a probability score anyone should trust.
→ see it in Resume–JD Matching 04The sentiment-analysis pipeline's location-normalization step cut unresolved geography from 72.8% to 43.85% before a single sentiment score was trusted. Cleaning happens before modeling, not after a bad result shows up.
→ see it across Experience$ cat experience.log
Barton Malow Featured
Pennsylvania State University
Oracle Corporation
Accenture Solutions Pvt Ltd
$ ls ./projects
Autonomous compliance agents detecting AI-driven regulatory risk in real time. Top 10 of 60+ teams, Penn State Nittany AI Challenge 2026. Hybrid architecture: deterministic rule engine + local LLM explanation layer with a 100%-deterministic fallback.
3-stage semantic ranking pipeline improving over keyword-based ATS matching: TF-IDF baseline → SBERT semantic embeddings → supervised neural refinement into a calibrated match probability.
End-to-end NLP + geospatial pipeline tracking global sentiment toward LLMs across 305K+ tweets. Location-normalization cut unresolved geography from 72.8% to 43.85%.
View repo →Deep Dive · Penn State Nittany AI Challenge, 2026
Fall-monitoring AI systems can quietly restrict resident movement in ways that violate federal nursing-home regulations, and nobody was watching for it in real time.
A missed compliance risk isn't just a fine, it's a resident's autonomy on the line. And a black-box LLM verdict isn't good enough when the finding has to hold up to an auditor.
A hybrid architecture: a deterministic rule engine maps directly to CMS regulations, a local LLM explains each finding in plain language, and a 100%-deterministic fallback means the LLM never gets the final say.
This is where principle 01 (a model never gets the final say) isn't an abstraction: every LLM explanation is checked against the deterministic rule engine before it's trusted, and every finding is SHA-256 audit-logged.
Deep Dive · Independent Project, 2026
Keyword-based ATS matching misses qualified candidates who describe their skills differently than a job description does, and over-ranks resumes that just repeat the right buzzwords.
A ranking system that can't tell semantic fit from keyword-stuffing produces bad shortlists on both ends: real candidates filtered out, weak ones let through.
A 3-stage pipeline that only adds complexity when it earns its place: a TF-IDF baseline first, then SBERT semantic embeddings, then a supervised neural layer that outputs a calibrated match probability instead of a raw similarity score.
This is where principles 02 (baseline before complexity) and 03 (AI accelerates the analysis, it doesn't replace the validation) aren't abstractions: TF-IDF proved the floor, SBERT proved the ceiling was higher, and every embedding-based match still had to earn its place against a supervised, labeled baseline before it became a trusted probability.
$ cat research.md
Accepted and presented at SEET 2026 (2nd International Conference on Software Engineering of Emerging Technologies), Penn State Behrend, PhD Research Track. Listed on page 76 of the conference abstract book. To be published in the official conference proceedings by Springer Nature.
View conference →Won the Prototype Phase and MVP Phase, advanced through funded phases to a Top 10 finish at the Pitch Contest, Hintz Family Alumni Center, University Park.
Recognized for exceptional contributions and high-impact performance on the Telia Company billing analytics engagement.
Representing students in discussions with university leadership; championing AI literacy and equitable AI access across Penn State's Commonwealth Caucus.
$ cat skills.yaml
Regression · Classification · Clustering · NLP · Time Series Forecasting · Feature Engineering · Model Evaluation (AUC, F1)
Python (Pandas, NumPy, Matplotlib, Seaborn, scikit-learn, TensorFlow, PyTorch, Keras) · SQL · R · Java
Claude Code · Claude Cowork · Claude Skills · Claude Projects · Claude MCP · ChatGPT · GitHub Copilot · Cursor IDE · Perplexity · Ollama · Google Gemini · Julius AI
Tableau · Power BI · Plotly · Matplotlib · Seaborn
Databricks · PySpark · Unity Catalog · Azure · AWS · Oracle Database · PostgreSQL · PL/SQL
Git · GitHub Actions · Jupyter · Jira
$ cat education.log
Master of Data Analytics · GPA 3.96/4
Statistical Analysis, Data-Driven Decision Making, Predictive Analytics, Deep Learning, Natural Language Processing.
B.Tech, Electronics & Instrumentation Engineering · GPA 3.3/4
Control Systems, Digital Electronics, OOP, Data Structures & Algorithms, Web Technology, Artificial Intelligence.
$ mail tushar
Open to full-time Data Scientist / Machine Learning Engineer roles starting Dec 2026.