Analyzed financial services dataset with ~10k records to identify customer churn patterns and key trends.

Utilized SQL functions like SELECT, WHERE, GROUP BY, HAVING, and WINDOW functions.

Built predictive models using logistic regression and random forest to forecast customer churn.

Presented findings in a written report and created Tableau visualizations to showcase actionable insights.

Extracted operational data from a SQL database to analyze performance metrics for the operations team.

Cleaned and transformed the dataset in Python to ensure data integrity and remove inconsistencies.

Designed an interactive Tableau dashboard, highlighting key KPIs like total requests, completion rates, and SLA compliance over a 6-month period.

Enabled stakeholders to identify trends, improve efficiency, and make data-driven decisions through actionable insights.

Built a recommendation system to cluster Toronto neighborhoods based on venue data using machine learning techniques.

Extracted and cleaned location data from Wikipedia and venue data from the Foursquare API.

Leveraged Python libraries like Pandas, NumPy, Scikit-learn, and Folium for data preprocessing, clustering, and visualization.

Applied K-Means clustering to segment neighborhoods, revealing patterns like high concentrations of coffee shops or gyms.

Designed an interactive map to present clusters, enabling users (e.g., movers, entrepreneurs) to make data-driven location decisions.

Developed a pricing model to predict bulldozer sale prices using historical auction data from Kaggle (~400K+ records).

Used SQL for time-based trend analysis with window functions, then transitioned to Python for deeper data cleaning and feature engineering.

Built and tuned a Random Forest Regressor, achieving strong R² performance and actionable predictions.

Visualized pricing trends and feature importances to help auctioneers and resellers forecast values and prioritize data collection.

Excel

Tableau

Python

SQL

Predictive Analytics

Developed models for energy forecasts, optimizing resource allocation and strategic planning precision.

Streamlined database processes to prevent unnecessary data refreshes thereby saving $12,000 annually through automation of manual billing tasks using SQL and Python scripts in HEX.

Developed Python automation tools to streamline large-scale data integration, enhancing AI model scalability and advanced analytics, resulting in the highest performance rating for outstanding contributions.

Partnered with engineering to integrate predictive analytics, aligning data insights with business objectives and strategies.

Developed financial models for an $8B portfolio, providing insights that shaped key investment strategies and enhanced returns.

Designed Tableau dashboards to monitor portfolio KPIs, empowering managers with agile, data-driven decision-making tools.

Performed market segmentation and variance analysis to identify high-value segments and boost portfolio allocation strategies.

Employed SQL to extract key metrics, delivering data-backed insights that optimized portfolio returns and minimized risks.

Integrated AI-driven insights into dashboards, reducing weekly analysis time and enhancing portfolio assessment efficiency.

Examined trading patterns for growth, creating actionable reports that supported strategic initiatives and informed decisions.

Conducted regression analysis on transaction data, identifying inefficiencies and implementing solutions, saving $80k yearly.

Analyzed customer behavior to create targeted recommendations, boosting satisfaction and enhancing service efficiency.

Built KPI dashboards for performance tracking, enabling stakeholders' insight into milestones and real-time decisions.

Applied statistical methods to forecast demand trends, optimizing service offerings and improving capacity management.

Performed A/B testing for marketing campaigns, refining effectiveness and driving engagement in targeted client segments.

Created KPI dashboard via Excel/SQL, reducing reporting time and enabling real-time tracking across multiple team projects.

Performed statistical analysis on client data, guiding managers to prioritize tasks, improving project outcomes significantly.

Led customer feedback analysis project, utilizing predictive insights to boost client retention, satisfaction, and reduce churn.

Examined resource allocation trends, uncovering cost-saving opportunities that enhanced budgeting and financial planning.

Crafted monthly metric visualizations, helping stakeholders grasp project ROI and align actions with strategic business goals.