Experienced IT Project Manager specializing in software development projects with a proven track record of successful delivery and team leadership. Skilled in agile practices and cross-functional collaboration.
Held roles overseeing project planning, execution, and stakeholder communication.
Developed applications in multiple programming languages and led development teams.
Experience implementing Scrum and Kanban practices to improve project workflows.
Directed multi-disciplinary teams to meet project milestones and deliverables.
GLOCAL
Led civic data projects using StatCan sources; cleaned/merged Core Housing Need (CHN 2016 & 2021) and Shelter-to-Income Ratio (STIR) tables in Python (pandas/Jupyter), building tidy, documented datasets for analysis., Built interactive Tableau dashboards (KPI overview, dual-year comparison, YoY change bars, province-level map, filters) to explain shifts in housing suitability and affordability across provinces., Performed data QA (type casting, missing-value rules, code-to-label crosswalks) and wrote reproducible notebooks to streamline future updates., Authored a policy brief translating findings into clear recommendations; results were used in a national-level report on affordability/suitability trends., Presented insights to program leads and partners, fielding questions on methodology, limitations, and policy implications.
Aditya Group of Companies
Built and optimized Tableau dashboards on 500K+ rows (PostgreSQL/MySQL), using LOD expressions, parameterized filters, and KPI drilldowns; tuned extracts/queries for performance, enabling near real-time insights and cutting reporting cycle time by 40% while boosting stakeholder self-service., Automated ETL/data-cleaning pipelines in Python (pandas, NumPy) with reusable notebooks and scheduled runs; standardized schemas, handled missing/outliers, added data validation + logging, and versioned with Git, improving data accuracy/consistency across projects and reducing manual errors by 35%., Implemented an ML workflow in scikit-learn: feature engineering, 80/20 train-test split, k-fold cross-validation, and hyperparameter tuning (GridSearchCV) for Random Forest and XGBoost; achieved a 15% lift in predictive accuracy with clear feature-importance explanations for business users.
Master’s of Management
Bachelor of Information Technology
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Deloitte
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