Architected and deployed an end-to-end commodity price forecasting platform on Databricks using MLflow, XGBoost, and CI/CD, registering 3 production model versions and reducing deployment cycle time by 40%.

Built a scalable ML inference application (React + Databricks Apps) supporting 1,000 concurrent users with <2s latency, enabling real-time enterprise-wide access to predictive insights.

Implemented automated MLOps workflows (model validation, versioning, Dev→Prod promotion), reducing production rollbacks by 30% and release errors by 45%.

Improved forecasting performance through advanced feature engineering and hyperparameter tuning, achieving an 18% reduction in MAPE relative to baseline time-series models.

Engineered an LLM-powered NLQ-to-BI visualization pipeline integrating Databricks Genie with Power BI, reducing manual report creation time by 60% and increasing self-service analytics adoption by 35%.

Automated SQL-to-visual translation and YAML <-> TMDL metadata conversion using Agent Skills and LLM orchestration, achieving 92% query-to-visual accuracy and reducing BI configuration effort by 50%.

Led a cross-functional team of 5 members in Agile sprints to build a data platform tracking over 1,500+ fentanyl analogs and precursor chemicals across public registries.

Collected and translated 10+ weekly partner inputs into prioritized backlog items and functional requirements, increasing sprint delivery efficiency by 30%.

Scraped and extracted chemical data from 10+ public sources (DEA, PubChem, ECHA) using BeautifulSoup and Selenium, creating a structured registry with 95%+ parsing accuracy.

Cleaned and normalized 5,000+ chemical records using Excel and Python, resolving duplicates and harmonizing synonyms to improve lookup precision by 40%.

Supported the development of a searchable fuzzy logic interface enabling identification by CAS, chemical names, or aliases, reducing search time for substances by 60%.

Led weekly sessions for 70+ students on Excel modeling using Frontline Solver simulations, to increase student accuracy in solving linear and mixed-integer optimization problems by 30%.

Facilitated interactive labs on Gurobi in Python, resulting in a 20% increase in student engagement scores.

Collaborated with the product team to design and implement data-driven solutions to optimize Ad Tech performance.

Set up 10+ A/B testing environments (weekly) using in-house tools to statistically evaluate creative templates,

improving recommendation accuracy by 50% and contributing to a 70% increase in campaign profitability.

Automated reporting and processing workflows using Python, Excel, and SQL, reducing manual effort by 50%.

Led end-to-end project execution across 5+ initiatives using Slack, ensuring 100% on-time delivery.

Mentored 10 junior analysts on ETL design, SQL querying, and business reporting tools, increasing productivity by 40%.

Developed stored procedures to automate ingestion and transformation of 5+ GB of ad-serving data into SQL Server using Python and Excel-based automation, reducing batch processing time by 50%.

Collaborated with engineering to integrate new ad platforms using REST APIs, increasing impression delivery by 20%.

Delivered weekly campaign performance insights and optimization reports to 10+ clients using Tableau/Excel, improving retention by 10%.