I'm a Data Science graduate student at San Francisco State University with hands-on experience in supply-demand forecasting, A/B testing, and ML model deployment. I've worked at companies like Clusteratech and SGN, delivering measurable business impact through data-driven solutions.
My expertise spans statistical analysis, causal inference, optimization algorithms, and responsible AI. I'm passionate about building scalable ML systems that solve real-world problems while maintaining ethical standards and business performance.
San Francisco State University • GPA: 3.56
Aug 2024 – Dec 2026 (Expected)
Python • R • SQL • Excel • Bash • Git • PySpark
Feature Engineering • Predictive Modeling • scikit-learn • TensorFlow • PyTorch • XGBoost
Hypothesis Testing • Regression • A/B Testing • CUPED • Power Analysis • Causal Inference • Diff-in-Diff
Databricks • Airflow • AWS • ETL • CI/CD • Model Monitoring
Tableau • Power BI • Looker • Matplotlib • Seaborn
📅 May 2025 – Aug 2025
📍 San Francisco, CA
Developed supply-demand forecasting models in Python/SQL/Excel on large-scale transactional datasets, improving rider-driver matching and reducing simulated wait times by 15%. Designed and executed pricing & incentive experiments using A/B testing + CUPED, generating statistically valid insights and achieving a rider conversion lift of +8%. Prototyped optimization algorithms (LP/MIP with OR-Tools) for driver allocation, boosting fulfillment rates during demand spikes by 12% and improving marketplace efficiency.
📅 Feb 2024 – Jul 2024
📍 Hyderabad, India
Built automated SQL-to-BI ETL pipelines supporting recommendation engines, raising CTR by 12% through more accurate targeting and personalization. Applied advanced causal inference methods (Diff-in-Diff, Propensity Score Matching) to evaluate incentive effectiveness, producing actionable insights for regional rollouts. Implemented ML model monitoring with drift detection, ensuring long-term stability, accuracy, and reliability of production systems.
📅 Jan 2023 – Jan 2024
📍 Hyderabad, India
Automated Python/SQL ETL pipelines processing millions of records, reducing reporting cycles by 40%. Executed A/B experiments with CUPED variance reduction, delivering insights that improved engagement and guided feature rollouts. Created Power BI dashboards and regression analyses, reducing anomaly detection time by 70%.