Turning complex data into business decisions, measurable outcomes and scalable analytics solutions.
I'm a Business Analytics professional with 6+ years of experience across business intelligence, operations, revenue analytics and entrepreneurship. Currently pursuing an M.S. in Business Analytics at the University of Massachusetts Amherst (May 2027).
My work connects business strategy with hands-on technical execution, from designing data pipelines and executive dashboards to building forecasting, optimization and machine learning solutions.
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| Area | Experience and impact |
|---|---|
| Business Intelligence | Built executive KPI dashboards and automated reporting to accelerate decision-making |
| Supply Chain & Operations | Analyzed vendor performance across 10+ suppliers and supported approximately $90K in savings |
| Data Engineering | Developed SQL and Snowflake pipelines, reducing decision latency by 40% |
| Entrepreneurship | Co-founded and scaled a B2B marketplace serving 100+ customers and 25 vendors |
| Forecasting & Analytics | Applied predictive analytics, optimization and business performance measurement to operational decisions |
The challenge: How can businesses balance uncertain demand, supplier capacity, inventory and fulfillment costs?
Built an end-to-end sales and operations planning platform using demand forecasting, mathematical optimization and financial reconciliation across a simulated 120-SKU supply network.
Tech: Python, SQL, DuckDB, OR-Tools, Streamlit
The challenge: How can businesses identify revenue leakage while ensuring that financial dashboards can be trusted?
Developed an analytics platform with governed revenue metrics, transaction-level reconciliation, customer risk modeling, automated data quality checks and executive dashboards.
Tech: Python, SQL, dbt, Snowflake, DuckDB, Streamlit, GitHub Actions
The challenge: How can businesses determine which marketing investments and experiments actually improve performance?
Developed an analytics platform combining campaign performance measurement, multi-touch attribution, A/B testing, funnel analytics and automated KPI governance.
Tech: Python, SQL, statistical analysis, experimentation, data visualization
The challenge: How can supply chain teams identify external disruptions early and prioritize emerging risks?
Built an AI-powered disruption intelligence system combining news collection, financial sentiment analysis and machine learning to identify potential supply chain risks.
Analyzed 572 news articles from nine sources, achieving 75.65% reported model accuracy in project evaluation.
Tech: Python, FinBERT, XGBoost, NLP, Plotly
| Domain | Tools and methods |
|---|---|
| Business Intelligence | Power BI, Tableau, Excel, KPI design, executive reporting |
| Programming & Analytics | SQL, Python, pandas, NumPy, statistical analysis |
| Data Engineering | Snowflake, AWS S3, Amazon Redshift, dbt, dimensional modeling |
| Machine Learning | scikit-learn, XGBoost, forecasting, NLP |
| Optimization | OR-Tools, demand planning, inventory and supply optimization |
| Data Applications | Streamlit, Plotly, automated reporting |
I work at the intersection of business intelligence, operations and modern data platforms, with particular interests in:
- Supply chain, procurement and inventory intelligence
- Revenue optimization and financial analytics
- Business intelligence and enterprise reporting
- Forecasting, experimentation and decision science
- Applied AI for business and operational automation
- Completing my M.S. in Business Analytics at UMass Amherst, graduating May 2027.
- Developing end-to-end cloud analytics and applied AI solutions.
- Open to full-time opportunities in business intelligence, supply chain analytics, operations analytics and related data-focused roles.