Amazon Externship via ExternI am a graduate Statistics student developing analytical and quantitative skills to move into applied data work.
A selection of academic projects and practice work that demonstrate statistical modeling, data analysis, and related programming skills completed during my Master's program.
Modeled score variance across exam difficulty tiers using Kernel Density Estimation (KDE) and identified high-risk demographic clusters linked to severe sleep deprivation. Delivered an end-to-end interactive Tableau dashboard supported by custom multi-variable SQL extraction queries.
Analyzed regional transmission dynamics across 21 Italian administrative areas, identifying logarithmic growth rates and outbreak peaks.
I am a graduate Statistics student developing analytical and quantitative skills to move into applied data work.
I am Francesco Bagnini, a Master's student in Statistics preparing to start my career. I have limited formal work experience and I am focused on building technical skills and project experience as I finish my graduate studies.
Externships
Amazon Operational Strategy & People Analytics Externship
In progress
Experience
Data Analyst Intern
ISTAT (Italian National Institute of Statistics) · May 2026 – July 2026
Field Survey & Data Collector
Modus (for Unicoop Firenze) · Sept 2023 – Apr 2024
Logistics & Operations Specialist
Classervices · Jan 2023 – Jan 2025[cite: 8, 9]
Education
Università degli Studi di Firenze
MSc in Statistics and Data Science
Università degli Studi di Firenze
BSc in Statistics (Laurea Triennale) · Class of 2024
Skills
Modeled score variance across exam difficulty tiers using Kernel Density Estimation (KDE) and identified high-risk demographic clusters linked to severe sleep deprivation. Delivered an end-to-end interactive Tableau dashboard supported by custom multi-variable SQL extraction queries.
Analyzed regional transmission dynamics across 21 Italian administrative areas, identifying logarithmic growth rates and outbreak peaks.
✅ Verified by Extern · ⏱️ In progress
Imagine shaping how AMAZON — yes, THE Amazon — welcomes and keeps its workforce. Every year, Amazon loses billions because new hires leave before Day 90. You’re going to fix that. In this externship, you’ll web scrape real-world employee reviews from across the web, extract actionable insights, and design persona-driven strategies to help Learning Ambassadors connect smarter, faster, and more humanely. It’s part research, part strategy—and all about learning how people data drives big decisions.
The externship investigated workforce retention at Amazon fulfillment centers by collecting and analyzing employee reviews and operational context. The work combined web-scraped sentiment and thematic analysis with persona development and strategic recommendations. Deliverables included cleaned datasets, thematic summaries, and persona profiles derived from employee feedback.
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I produced a structured spreadsheet mapping Fulfillment Center roles (for example Learning Ambassador, Picker, Packer, Area Manager) with responsibilities, collaborators, tools, and annotated notes.
The project collected role-level data at an Amazon Fulfillment Center, listed each role, main responsibilities, who they worked closely with, common tools, and notes. The deliverable produced a structured spreadsheet summarizing roles like Learning Ambassador, Picker, Packer, and Area Manager.
The project received a raw Glassdoor reviews CSV, I inspected columns, removed low-value or mostly missing fields (e.g., count_helpful, flag_covid, career_opportunities_rating), cleaned the table with pandas, and exported the cleaned CSV.
