Product Data Analyst (Academic Background)

Mexico, Brazil, Colombia, ArgentinaRemotefull-timemid

Product Data AnalystData & AI

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Overview

Job Title: Product Analyst (Academic Background) Location: Remote (LATAM)Working Hours: Monday to Friday, 9:00 AM – 5:00 PM PSTCompensation: $3,500 – $4,

Technical Tools

Product Data AnalystData & AI

About the Role

~1 min read

Develop Python scripts for data processing, automation, and statistical analysis

Build and maintain Fresh Tri’s Snowflake data warehouse, including schema design and data ingestion

Create and maintain BI visualizations for stakeholders

Partner with Engineering to ensure data quality and pipeline reliability

Conduct qualitative user research (e.g., interviews) to complement quantitative data
Support academic and business reporting with actionable insights
Write methods and results sections for research manuscripts
Perform advanced statistical testing for ad hoc analysis
Lead or support grant-funded projects and applications
Design and execute statistical analysis plans
Clean, merge, and prepare datasets for analysis
Conduct literature reviews and support commercial and client research

Requirements

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2+ years in Product Analytics, Growth Analytics, Data Analytics, or a similar role supporting digital products
Strong experience with Mixpanel (dashboards, event tracking, taxonomy, validation)
Proficiency in SQL for structured and unstructured data
Experience using Python for analysis, automation, or data processing
Experience with Snowflake, including pipelines, data sources, and visualizations
Solid foundation in statistics, including experiment design, hypothesis testing, and regression
Ability to read and understand application code for debugging analytics instrumentation
Experience with Google Analytics, A/B testing tools, and experimentation frameworks is a plus
Bachelor’s degree in a technical, quantitative, or behavioral science field preferred
Strong communication skills — able to translate complex analysis into clear business insights
Curious, collaborative, and impact-driven