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Financial Data & AI Senior Director | CQF certified
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Assessing your cultural and operational fit
Data-driven and self-motivated professional, with strong team-player attitude. Combining 10+ years of Data Science experience with strong Machine Learning theoretic background. Passionate about data-driven decision making, Machine Learning and its applications to real life, especially Financial Markets.
CQF Institute
CQF, Certificate in Quantitative Finance
January 1, 2023 – September 1, 2023
INSEAD
Executive Education, Competitive Strategy
January 1, 2015 – January 1, 2015
MIT Sloan School of Management
Executive Education, Revitalizing Your Digital Business Model
January 1, 2015 – January 1, 2015
IESE Business School
Executive Education, Developing Leadership Competences
January 1, 2015 – January 1, 2015
Utrecht University
Master’s Degree, Technical Artificial Intelligence
January 1, 2011 – January 1, 2012
Politecnico di Milano
Master’s Degree, Computer Engineering
January 1, 2010 – January 1, 2012
Politecnico di Milano
Bachelor’s Degree, Computer Engineering
January 1, 2007 – January 1, 2010
SIAT - Società Italiana Analisi Tecnica
Faculty Lecturer: AI for Finance
September 1, 2023 – Present
Milan, Lombardy, Italy · On-site
BIP
Data & AI Senior Director | Financial Services
April 1, 2023 – Present
Milan, Lombardy, Italy
BIP
Principal Data Scientist, Alliances and Digital Assets Manager
April 1, 2022 – April 1, 2023
Milan, Lombardy, Italy
BIP
Lead Data Scientist, Alliances and Digital Assets Manager
September 1, 2020 – April 1, 2022
Milan, Lombardy, Italy
OCBC Bank
AI Lab - Artificial Intelligence Lead
July 1, 2019 – September 1, 2020
OCBC Bank
Innovation Lab - Artificial Intelligence Lead
October 1, 2017 – July 1, 2019
Business Integration Partners S.p.A.
Lead Data Scientist
April 1, 2016 – September 1, 2017
On-site
Business Integration Partners S.p.A.
Senior Data Scientist
April 1, 2015 – April 1, 2016
On-site
Business Integration Partners S.p.A.
Data Scientist
January 1, 2014 – April 1, 2015
On-site
Reply S.p.A.
Information Security Data Scientist
February 1, 2013 – December 1, 2013
Milan, Lombardy, Italy
DataTrader (side project)
ML Quant Portfolio Manager
January 1, 2013 – Present
Everywhere · Remote
Sentient
Machine Learning Researcher on Stock Markets
April 1, 2012 – November 1, 2012
Amsterdam, Paesi Bassi
Machine Learning evolution strategy for trading strategy optimization
February 1, 2021 – August 1, 2021
Machine Learning evolution strategy for trading strategy optimization.
Financial portfolio risk management framework
October 1, 2020 – August 1, 2021
Financial portfolio risk management framework.
Reports Analyzer
April 1, 2018 – September 1, 2018
Web tool based on text mining and analytics features to analyze and compare text documents. Core engine developed in Python and APIs developed in Python Flask.
AI stock portfolio manager
February 1, 2018 – Present
AI fully based stock portfolio manager completely developed from scratch. Evolution strategy is the main Machine Learning technique used at the core of the project to optimize stock selection and weighting at each rebalancing period. Long only and long/short strategies accurately tested (taken into account trading costs, slippage, survival bias, etc) on international stocks, US stocks, SG&HK stocks and futures. Results show consistent and significantly better results than benchmarks. Project developed in Pyhton in Jupyter notebook.
Next-Day Stock Return Classification Using Sentiment Analysis (MSc Thesis)
April 1, 2012 – November 1, 2012
(Master thesis project in Computer Engineering) This study investigated to what extent Sentiment Analysis can be effective on the stock trading process. Statistical tests, classification techniques and trading simulations were used within the research. Results showed that Sentiment Analysis is an influencing factor in classifying next-day stock returns. Moreover, profits achieved by trading simulations indicated that the developed trading strategy based on next-day stock return classification and Sentiment Analysis is profitable. Project developed in R, Weka, RapidMiner, MySQL and Perl.
Evolutionary Algorithms Implementation
November 1, 2011 – February 1, 2012
Implementation of Evolutionary Computing algorithms (e.g. Iterated Local Search, Genetic Local Search and Adaptive Genetic Local Search). Project developed in Java within HyFlex framework.
Data Mining Classification Algorithms Implementation
September 1, 2011 – November 1, 2011
Implementation of classification algorithms (e.g. classification trees). Project developed in R.
Scheduling and Resource Binding with Markovian Decision Processes (MDPs)
August 1, 2011 – November 1, 2011
In the context of multi-objective Design Space Exploration (DSE) optimization in hardware design, the project aims to implement scheduling and resource binding exploiting Markovian Decision Process (MDPs). Project developed in C++.
Prototype of a MDP Decisional Model for Adaptive Systems (BSc Thesis)
September 1, 2009 – September 1, 2010
(Bachelor thesis project in Computer Engineering) Implementation of a decisional model based on Markov Decision Processes to decide whether to execute a task on CPUs or FPGAs. Project developed in C and Lisp.
How To Pick Cheap Stocks - 10 Simple Rules
Udemy
June 24, 2026 – Present
Computational Investing
Georgia Institute of Technology
June 24, 2026 – Present
Options Trading for Rookies: Understand Options Completely
Udemy
June 24, 2026 – Present
Google Cloud Professional Machine Learning Engineer
Google Cloud - Minnesota
June 24, 2026 – Present
Tackling the Challenges of Big Data
MIT Professional Education
June 24, 2026 – Present
Developer Training for Spark and Hadoop
Cloudera
June 24, 2026 – Present
Cognitive computing platform (Deep Learning)
Vendor
June 24, 2026 – Present
The beginner guide to futures trading
Udemy
June 24, 2026 – Present
Process Mining Sales Professional Certification
Celonis
June 24, 2026 – Present
Upper Intermediate English
Arena School of Dublin, Dublin
June 24, 2026 – Present
Options Trading for Rookies: Turn Losing Trades into Winners
Udemy
June 24, 2026 – Present
Options Trading for Rookies: Complete Guide to Stock Options
Udemy
June 24, 2026 – Present
Python REST APIs with Flask, Docker, MongoDB, and AWS DevOps
Udemy
June 24, 2026 – Present
Investment Management with Python and Machine Learning Specialization
Coursera
June 24, 2026 – Present
TOEFL iBT English Exam
ETS
June 24, 2026 – Present
TOEIC English Exam
ETS
June 24, 2026 – Present
Deep Learning in Python
DataCamp
June 24, 2026 – Present
Python for Financial Analysis and Algorithmic Trading
Udemy
June 24, 2026 – Present
Understanding Bonds
Udemy
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from academic theses to personal projects and professional roles, shows a strong passion for AI and Machine Learning, especially in the financial domain. Their continuous learning through certifications (CQF, Deep Learning, Python for Finance) and executive education indicates a proactive and growth-oriented mindset. The target role of ML Engineer aligns well with their deep technical background and practical experience in applying ML to complex problems. Their experience in consulting and banking environments suggests adaptability to different organizational cultures.
Soft Skills & Operational Fit
The candidate's experience in leadership roles, stakeholder engagement, and team management (forming, coaching, managing) indicates strong soft skills. Their involvement in client communication and strategic initiatives suggests good operational fit for roles requiring both technical depth and business acumen. The psychometric test results are not provided, so a full assessment of logical reasoning, work attitude, stress handling, and team collaboration cannot be made.