Data Analyst | Data Science & Machine Learning | Business & Customer Analytics
A data professional focused on analytics, data science, and AI, using Python, SQL, statistics, visualization, and machine learning to build practical, data-driven solutions. I turn complex, messy data into clear insights, actionable recommendations, and practical decision-support tools. My current focus is on customer and user analytics — understanding customer health, retention, churn, and value, and turning those insights into recommendations businesses can act on.
I enjoy investigating problems beyond the surface: understanding what's happening, identifying the root cause, and determining what should happen next to drive business growth.
A deployed application that scores customer risk two ways — a transparent rule-based model and a predictive model — then combines risk with predicted lifetime value to answer the question that matters: which at-risk customers are really worth chasing, and is acting on it worth the cost?
Built with: Python · Pandas · scikit-learn · SQL · DuckDB · Streamlit · Docker
🔗 Repository · 🚀 Live App
- Customer Health Scanner — risk scoring + predictive modeling + ROI-based prioritization (10,000 customers scored)
- Cohort Retention Analysis — when and why customer drop-off happens (1,000 users, 10 monthly cohorts)
- Customer Churn Analysis — what's statistically linked to real churn (7,000+ real telecom customer records)
- Customer Lifetime Value Analysis — where historical value estimates mislead (7,000+ real telecom customer records)
- CAC & Payback Analysis — which acquisition channels are worth the spend (5-channel, 6-month simulation)
- Pricing Page A/B Test — testing product changes with statistical rigor (11,000-user experiment)
I like understanding the bigger picture, finding root causes rather than symptoms, and turning messy systems into something that actually works the way it's supposed to. That mindset is what drew me to data in the first place.
I'm deliberately expanding beyond analytics into machine learning, data engineering, MLOps, and AI engineering — building toward end-to-end capability, from raw data through to a deployed solution. I'm expanding my focus on data science and machine learning. I'm building toward end-to-end data and AI capabilities—from raw data and analysis to predictive models and deployed solutions.
Data Analyst/Data Scientist · Customer/Product Analytics Analyst · Business Analyst roles.
Understanding the data. Finding the root cause. Building what comes next.