VortexifyAI · YC F24
Forward Deployed Engineer (Client-Facing Analytics)
Jan 2026 – Mar 2026
Reconciled fragmented, ambiguous ERP data across 2-3 client companies into a single integration layer, cutting manual reporting time by 30% and enabling real-time supply chain visibility.
- Identified the relevant datasets amid ambiguous, fragmented ERP sources and reconciled them using SQL joins across multiple systems, catching data quality issues that had gone undetected
- Built a safety stock dashboard from scratch to catch root causes behind recurring inventory discrepancies, presented it directly to client C-suite executives alongside the founder, and got new KPI standards adopted team-wide
The Data Exchange Purdue
Co-Founder & Project Manager
Mar 2025 – Jan 2026
Led end-to-end quantitative analysis engagements across 4-5 client organizations, from KPI definition through model validation to final delivery.
- As Project Manager, led training sessions to onboard new team members and partnered directly with cross-functional business stakeholders across every engagement
- Presented findings to technical and non-technical audiences via Tableau dashboards adopted directly into client decisions
Syngenta · The Data Mine
Data Science Research Intern
Jan 2025 – May 2025
Built and validated ML models across 3 of the company's 4 growing regions, packaging results into a recommendation system used for regional product decisions.
- Built and validated GBLUP and LightGBM models, finding LightGBM consistently outperformed GBLUP on product placement accuracy
- Worked hands-on with Syngenta's internal operational and genetic datasets through onsite briefings at their Malta, IL facility via Purdue's Data Mine program
HUMN Capital
Data Science Intern
Aug 2024 – Dec 2024
Cleaned and organized large operational datasets, resolving missing or inconsistent values in 65% of records to improve data accuracy for managerial insights.
- Cleaned and organized datasets using SQL, R, and Python; developed Python dashboards with Matplotlib and SQL reports to visualize trends in revenue and user behavior
- Supported the NLP team by analyzing text data with Python libraries to train sentiment analysis models for enhanced business insights
Purdue Dept. of Statistics
Undergraduate Researcher, Adversarial ML & NLP/LLM Evaluation
Aug 2024 – Present
Research spanning adversarial ML and LLM robustness, under Prof. Bowei Xi at Purdue Statistics.
- Second author on an upcoming peer-reviewed paper modeling adversarial ML as a Stackelberg game, and contributing author on a forthcoming CRC Press book chapter surveying adversarial attacks against NLP models and LLMs (jailbreak, prompt injection, backdoor) and current defenses
- Implemented adversarial attack frameworks in PyTorch for model behavior evaluation and LLM robustness testing, building anomaly detection, troubleshooting, and failure-mode analysis directly into the evaluation pipeline