Quantitative Analyst presso Eni Plenitude
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City St George’s, University of London
Master of Science (M.S.), Data Science
January 1, 2015 – January 1, 2016
Università degli Studi di Firenze
Laurea Magistrale LM (MSc)
January 1, 2011 – January 1, 2014
Università degli Studi di Firenze
Laurea triennale (BSc)
January 1, 2007 – January 1, 2011
Plenitude
Quantitative Analyst - Energy Management
June 1, 2020 – Present
Milan, Lombardy, Italy
Edison SpA
Data Scientist
April 1, 2019 – June 1, 2020
Greater Milan Metropolitan Area
Advanced Global Solution AGS S.p.A.
R&D Data Scientist - Machine Learning Engineer
September 1, 2016 – March 1, 2019
GE Oil & Gas
Junior Engineer
November 1, 2014 – July 1, 2015
Florence, Tuscany, Italy
Fiorentina Nuoto
Swimming Instructor
March 1, 2008 – October 1, 2011
Florence, Tuscany, Italy
A Heat-Jarrow-Morton framework for energy markets: a pragmatic approach
February 1, 2023 – Present
We discuss the application of the Heat-Jarrow-Morton framework Heath to energy markets. We focus on the markets' structure, model calibration by dimension reduction with Principal Component Analysis, Monte Carlo simulations and derivatives pricing. As application, we focus on European power and gas markets: we calibrate the model on historical futures quotations, we perform futures and spot simulations and we analyze the results.
Classifying Hand Written Digits with Deep Neural Networks
May 1, 2016 – Present
We tested two different architectures of deep neural network, performing classification on the hand written digits of the MNIST dataset. In the first architecture a dimensionality reduction on the input data has be achieved by the mean of two Autoencoders operating in series, then the output of the second Autoencoder has been used as input for a Softmax Layer, trained in supervised fashion. In the second architecture the dimensionality reduction has been achieved using a Self Organizing Map (SOM), whose output has been sent to a Multilayer Perceptron (2 hidden layers) trained with backpropagation. Models as well as the training algorithms have been implemented in Matlab, without the use of Matlab built-in functions.
Large Scale Text Classification (Apache Spark)
December 1, 2015 – Present
Analysis of text collection of project Gutenberg (www.gutenberg.org).The aim of the work was first to load, parse and process large-scale unstructured and structured data.The next step was the development of classifiers to recognise the subject of a given text. Thirdly it was about evaluating the performance of the classifiers in terms of classification accuracy as well as resource usage. All the computational part of the project has been performed using Apache Spark.
IELTS Overall Score: 7.5
British Council
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from energy market frameworks to deep neural networks for image classification and large-scale text classification, indicates a broad interest in data science applications. The roles held (Quantitative Analyst, Data Scientist, Machine Learning Engineer) align well with a Data Analyst target role, especially one requiring advanced analytical and modeling capabilities. The focus on energy markets and financial modeling suggests a fit for industries requiring specialized domain knowledge. However, the projects are all personal, and there is no explicit mention of team-based project work outside of professional roles, which could be a minor area for further exploration regarding collaborative cultural fit.
Soft Skills & Operational Fit
The candidate's experience as a supervisor and guide for junior team members indicates leadership potential and mentoring skills. Working with Scrum methodology suggests adaptability to agile environments. The description of developing and deploying tools implies a focus on practical, deployable solutions. Experience in an international team suggests good collaboration skills.