Passionate about dimensionality reduction through factor analysis in psychometric data and building pipelines that prevent data quality issues at the source.
I love logical and ambiguous problems where I can optimize and refactor code to make it load 3x faster.
Statistician with 4+ years of experience building automated data pipelines, scalable models, and self-service BI to solve complex business problems.
I specialize in turning manual, fragile processes into automated, scalable systems using SQL, Power BI, Power Automate, Python, and PySpark.
Key Achievement: Migration Automation with Power Automate + Power Apps
Situation: Users without OneStream licenses lost access to financial reports during Hyperion to OneStream migration
Action: Built a Power Automate flow + Power Apps to automatically identify, track, and categorize 300+ files in SharePoint
Result: Reduced file retrieval time by 90% and enabled delivery of Power BI reports for 300+ files
Key Achievement: ETL Process Migration + Power BI IP
Situation: Manual ETL process in Alteryx with no API available
Action: Migrated to SQL Dataflows in Power BI Service and documented Power BI best practices
Result: Decreased data transformation effort by 60% and left behind IP: a Power BI best practices guide
Key Achievement: Custom Workforce Model for Canada
Situation: No standardized system to calculate employee benefits across 10 Canadian provinces
Action: Designed a custom workforce model with business logic for taxes and benefits
Result: Reduced labor cost projection time and delivered tool adopted by HR team
Key Achievement: Interactive SDG Dashboard
Action: Built ETL in Power Query with MySQL connection and developed a multi-page Power BI Dashboard
Features: Buttons, bookmarks, dropdown filters, custom visuals, and variable normalization for SDG tracking
Result: Delivered interactive tool for monitoring Sustainable Development Goals
Key Achievement 1: Psychometric Data Pipeline with Python
Used Python to normalize student lists and conducted a census to obtain exact student counts per school. Collected informed consent and applied questionnaires to measure satisfaction, procrastination, and motivation.
Performed Exploratory Factor Analysis and Confirmatory Factor Analysis. Built a linear regression model using created indices to predict student satisfaction.
Key Achievement 2: Automated PDF Report Generation
Developed an automated pipeline to generate PDF lists for each school including student and parent/guardian information. Streamlined administrative reporting.
Measuring working memory, short-term memory, and attention. Design Variables: Music noise levels, time of day when test was conducted. Designed and implemented a quasi-experimental study to analyze cognitive performance.
Correlation analysis between Big Five personality traits, logical reasoning, admission score, and GPA. Variables: Anxiety, fear of negative evaluation, health factors like exercise, and wellbeing factors like stable employment.