Claudio Proietti Mercuri

Leveraging my academic background and industry experience, I'm enthusiastic about applying my skills in the field of Data Science and Machine Learning. I am keen on opportunities that not only challenge me but also enable me to provide insightful, data-driven solutions that assist organizations in making the most of their data. (Programming Languages: Python & R, Visualization tool: Tableau)

- Academic Background:
I hold a Bachelor's degree in "Economics, Business Sciences, and Business Administration" from La Sapienza University, Rome (2017), followed by two Master of Science degrees from the University of Maastricht, "SBE" in "Financial Economics" (2022), and "Business Intelligence and Smart Services" (2023).
My main university background is in Financial models & analysis, Statistical applications, Natural Language Processing (NLP), Business Analytics, Business Intelligence, Tableau, Programming languages: Python, R, and LaTeX, Economics (Micro & Macro), Accounting, and Management.

- Work background:
In February 2018, shortly after my BCs and before my Master's, I joined the U.N. - World Food Programme (WFP) as Finance Assistant first, and as a junior consultant later, in Finance Contribution Accounting and Donor Financial Reporting Branch (FINC), in which I worked on the most finance Donor Reports - Standard Annual Projects (SPR) and Annual Country Reports (ACRs).
During these years I've also had the opportunity to work with corporate ERP systems (SAP Wings FM/GM) and Business Objects reporting tool and with Microsoft Office 365 package (Word, Excel, PowerPoint, Outlook).

In addition, during High School, I worked in a para-pharmacy as a clerk, sales, and shipments tracking assistant, and as a rafting guide and canoe-kayak instructor at Vivere L'Aniene S.n.c.

Programming Languages: Python & R

Visualization tool: Tableau
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Present: Master Course Tutor
Course: “Research in International Business”. 
Main topic: IB research methodologies and designs, R programming language. 

Author of a research project at Maastricht University. Title:
"NLP adoption through Word Embedding in Measuring Human Risk Perception". 
Description: This project delves into the capabilities of the Word2Vec model in capturing and predicting human risk perception, extending the work of Bhatia (2019). The initial focus is on the effectiveness of Word2Vec 300-dimensional vectors in reaching a comparable performance to the psychometric approach which relies on time-intensive and costly human interviews. Subsequently, it is conducted a novel study investigating how including information from similar word-vectors and performing algebraic operations on the semantic space could enhance predictive power.

In February 2018, shortly after my BCs and before my Master's, I joined the U.N. - World Food Programme (WFP) as Finance Assistant first, and as a junior consultant later, in Finance Contribution Accounting and Donor Financial Reporting Branch (FINC), in which I worked on the most finance Donor Reports - Standard Annual Projects (SPR) and Annual Country Reports (ACRs).
During these years I've also had the opportunity to work with corporate ERP systems (SAP Wings FM/GM) and Business Objects reporting tool and with Microsoft Office 365 package (Word, Excel, PowerPoint, Outlook).

In addition, during High School, I worked in a para-pharmacy as a clerk, sales, and shipments tracking assistant, and as a rafting guide and canoe-kayak instructor at Vivere L'Aniene S.n.c.

I hold a Bachelor's degree in "Economics, Business Sciences, and Business Administration" from La Sapienza University, Rome (2017), followed by two Master of Science degrees from the University of Maastricht, "SBE" in "Financial Economics" (2022), and "Business Intelligence and Smart Services" (2023).
My main university background is in Financial models & analysis, Statistical applications, Natural Language Processing (NLP), Business Analytics, Business Intelligence, Tableau, Programming languages: Python, R, and LaTeX, Economics (Micro & Macro), Accounting, and Management.