Résumé
Alex Rivera
Senior data analyst with 5+ years turning messy, large-scale data into clear decisions. Background spans analytics engineering, A/B testing, machine learning, and data storytelling for growth and operations teams.
Work Experience
Meridian Analytics · New York, NY (Hybrid)
Lead analyst for the growth and monetization team, responsible for experimentation infrastructure, dashboarding, and ad-hoc analysis supporting product and marketing decisions for a B2B SaaS platform with $140M ARR.
Responsibilities
- Designed and maintained the company's A/B testing framework, running 30+ concurrent experiments with proper power analysis and multiple comparison corrections
- Built and owned 14 Looker dashboards consumed by C-suite and board-level stakeholders
- Developed Python-based anomaly detection system reducing time-to-alert for KPI degradation from 48 hours to under 4 hours
- Mentored two junior analysts, conducting weekly code reviews and data modeling guidance
- Partnered with engineering to instrument event tracking for 3 new product features
Key Achievements
- Identified pricing page friction through funnel analysis; AB test on checkout flow lifted conversion by 17.3%
- Built customer health score model (Snowflake + dbt) reducing churn by 14% over 6 months
- Reduced data warehouse compute costs 41% through query optimization and model restructuring
Veritask Inc. · Boston, MA (Remote)
First analytics hire at a Series A logistics startup. Built the data infrastructure from scratch and delivered insights supporting operations, customer success, and executive reporting.
Responsibilities
- Stood up the company's first data warehouse (Google BigQuery) and ETL pipelines using Airflow and Fivetran
- Created weekly operations report automating manual processes that previously took 6+ hours
- Conducted geospatial analysis (GeoPandas) to optimize delivery route clustering, reducing median delivery time by 11%
- Collaborated with the product team to define and instrument KPIs for a new carrier portal launch
Key Achievements
- Designed and implemented churn early-warning system for 200+ enterprise accounts, flagging at-risk accounts 45 days before contract end
- Built regression model forecasting package volume ±8.3% accuracy, enabling better carrier capacity planning
- Delivered investor data package used in $22M Series B fundraising round
Education
B.S. in Statistics with a minor in Computer Science. Graduated with High Distinction (GPA 3.87). Relevant coursework included Statistical Computing, Applied Regression, Machine Learning, Database Systems, and Time Series Analysis.
Highlights
- Senior thesis: "Detecting Structural Breaks in Financial Time Series Using CUSUM and Bayesian Change-Point Methods" — presented at the Michigan Undergraduate Research Symposium
- Teaching Assistant for STATS 250 (Introduction to Statistics) for 3 semesters
- Member of the Michigan Data Science Team (MDST), competing in national Kaggle competitions
Key Courses
| Course | Topics |
|---|---|
| STATS 413 | Applied Regression Analysis |
| STATS 425 | Introduction to Probability |
| STATS 531 | Time Series Analysis |
| EECS 445 | Introduction to Machine Learning |
| EECS 484 | Database Management Systems |