ABOUT
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.
EDUCATION
M.S. Statistical Data Science
San Francisco State University • GPA: 3.56
Aug 2024 – Dec 2026 (Expected)
PROGRAMMING & DATA
MACHINE LEARNING
STATISTICS & EXPERIMENTATION
DATA SCIENCE OPS
VISUALIZATION & BI
Work Experience
Clusteratech
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.
SGN (Suguna Media Network)
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.
Headlines Media Group of Publications
Automated Python/SQL ETL pipelines processing millions of daily records, reducing reporting cycles by 40% and enabling real-time analytics access. Executed A/B experiments with CUPED variance reduction, delivering insights that improved engagement and guided feature rollouts. Created interactive Power BI dashboards and conducted trend/regression analyses, reducing anomaly detection time by 70% and improving executive decision-making.
Featured Projects
Built a machine learning pipeline to predict flight delays using schedule and weather data. Applied classification, regression, and feature engineering to model delay likelihood and duration. Achieved 73% accuracy in binary classification.
Customer Churn Analysis using Python with data preprocessing, exploratory analysis, and machine learning models. Achieved 97% accuracy with Random Forest Classifier. Includes comprehensive EDA and feature engineering.
A hands-on project demonstrating the use of Fairlearn, SHAP, and Aequitas to audit and mitigate bias in machine learning models, particularly in hiring scenarios. Focus on ethical AI practices.
A research tool that combines OpenAI, FAISS, and LangChain to analyze and retrieve insights from news articles. Enables fast semantic search and automated summarization using LLMs.
An intelligent, NLP-powered e-commerce product recommendation system using RAG techniques and Streamlit. Users can input custom preferences to receive semantic, personalized product suggestions.
Machine learning-based web application that predicts the likelihood of three major diseases: Diabetes, Heart Disease, and Parkinson's Disease. Using Scikit-learn with interactive web interface.
An intelligent query processing system using transformer embeddings and FAISS vector search for hospitality/dining queries, reducing irrelevant retrievals by 22%. Features semantic similarity matching and advanced NLP.
A production-ready machine learning pipeline for predicting Click-Through Rates and Conversion Rates using XGBoost and PyTorch, achieving AUC = 0.91+ on 10M+ samples. Features intelligent ranking systems.
Implement personalized local services search platform with ML-powered ranking algorithms. Features advanced recommendation systems and personalized search results for local service discovery.
React.js and Node.js web app that helps users explore cities, book restaurant reservations, and find optimized travel routes using the Traveling Salesman Algorithm. Integrated with real-time data APIs.
Forecast demand and optimize the supply chain, reducing inventory costs while avoiding stockouts. Features advanced time series forecasting and optimization algorithms for supply chain management.
Certifications
Professional certifications that validate my expertise in data science and AI technologies.
Google Professional Data Analytics Certificate
Comprehensive program covering data analysis, visualization, and statistical methods using industry-standard tools.
HuggingFace Generative AI & Transformers Program
HuggingFace
Advanced training in generative AI, transformer architectures, and natural language processing applications.
Leadership & Involvement
Committed to fostering growth in the data science community through mentorship, leadership, and active participation in professional organizations.
Teaching Assistant - Data Visualization
San Francisco State University
Mentored 25+ graduate students in advanced data visualization techniques, statistical analysis, and dashboard design. Conducted weekly office hours and graded assignments for complex visualization projects.
Impact: Improved student comprehension scores by 20% through personalized guidance
Vice President, Analytics Club
San Francisco State University
Led strategic planning and organized technical workshops on SQL, AWS, and machine learning for 100+ attendees. Coordinated industry speaker events and networking sessions with data science professionals.
Impact: Increased club membership by 40% and established partnerships with 5 tech companies
Participant - Women in Data Science (WiDS)
WiDS San Francisco 2025
Active participant in the global Women in Data Science conference, engaging with cutting-edge research presentations, networking with industry leaders, and contributing to diversity initiatives in data science.
Impact: Built network of 50+ female data scientists and contributed to 3 diversity panels