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Machine Learning Manager / Senior Machine Learning Lead
- Result driven machine learning manager: 5+ years of experience leading a high-performing team to develop cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) solutions in the Equities and Execution Services space - Lead ML / AI modelling quant: 7+ years of experience overseeing, developing and improving the development of ML models for revenue generation. - Research Scientist: 8 years of experience as a member of leading academic groups with 30+ publications in top AI journals and conferences. - Proven ability to align technical innovation with business objectives, fostering collaboration between data science, engineering, and trading teams. - Proven track record of aligning product goals with business strategy and navigating complex regulatory environments.
University of Southampton
Doctor of Philosophy (Ph.D.), Computer Science
January 1, 2009 – January 1, 2012
Sapienza Università di Roma
Laurea Specialistica, Ingegneria Informatica - Sistemi di Intelligenza Artificiale
January 1, 2004 – January 1, 2007
Sapienza Università di Roma
Laurea, Ingegneria Informatica
January 1, 2001 – January 1, 2004
Lycee Chateaubriand
Highschool Degree, Scientific Studies
January 1, 1987 – January 1, 2000
Two Sigma
Senior Vice President
January 1, 2026 – Present
London Area, United Kingdom · On-site
Goldman Sachs
Team Lead - EMEA Applied AI
January 1, 2022 – October 1, 2025
Goldman Sachs
Executive Director: Senior Machine Learning / Quant Scientist
January 1, 2021 – February 1, 2022
Goldman Sachs
Strategist - Machine Learning / Quant Scientist
September 1, 2017 – January 1, 2021
Mind Foundry
Machine Learning Research Scientist
August 1, 2016 – July 1, 2017
Greater Oxford Area
Disney Research
Associate Research Scientist
November 1, 2014 – July 1, 2016
Greater Pittsburgh Region
University of Southern California
Research Associate (postdoc)
October 1, 2012 – November 1, 2014
Los Angeles
University of Southampton
Visiting Scholar
September 1, 2012 – December 1, 2013
University of Sydney
Visiting Student
February 1, 2011 – May 1, 2011
Australian Center for Field Robotics (ACFR)
University of Southampton
Postgraduate Researcher
May 1, 2009 – September 1, 2012
University of Rome "La Sapienza"
Research assistant
January 1, 2008 – January 1, 2009
Dipartimento di Informatica e Sistemistica "Antonio Ruberti"
CONSIGLIO NAZIONALE DELLE RICERCHE - CNR
Student Collaborator
September 1, 2007 – January 1, 2008
Rome
PAWS
November 1, 2013 – Present
The aim of this project is to develop a system to generate patrols for protecting endangered species from illegal poaching, uncontrolled extraction and / or harvesting (e.g., tigers, fisheries, pristine forests). I am the team leader of the wildlife project. I am working with a team of PhD students to develop the system and test it in the real world. One key difference with my previous project (TRUSTS) is the possibility to exploit the behavior of poachers and use machine learning to improve the scheduling system.
TRUSTS
November 1, 2013 – Present
My work in this project was that of the team leader. I have supervised a team of students and researchers to develop a system for generating intelligent randomized schedules to help the Los Angeles Sheriff's Department patrol the Los Angeles Metro Transit System. Our system, TRUSTS is composed of a centralized planner and of a smartphone application which can be used to visualize a schedule and to collect data. To evaluate this system, we also design a large scale real-world experiment whereby we tested the system in the field, in collaboration with the Los Angeles Sheriff's Department.
Orchid Project
September 1, 2011 – Present
My work in the project consited in developing ARGUS: a potential system for coordinating teams of unmanned aerial vehicles to provide live aerial imagery to the first responders at the scene of a disaster.
Aladdin Project
January 1, 2005 – December 1, 2010
My work in this project consisted in developing new algorithms for coordinating teams of multiple agents in a robust and decentralised fashion. In particular, I focused on generalising the well known known max-sum algorithm for coordinations for problems involving stochastic functions, or functions involving multiple conflicting objectives.
Cultural Fit Analysis
The candidate's background is heavily skewed towards AI/ML research and quantitative roles in finance, which indicates a strong analytical and results-driven mindset. The project diversity, ranging from academic research to real-world applications in security and finance, suggests adaptability. However, the target role of 'Data Analyst' might be a step down from their extensive experience as a Senior Machine Learning/Quant Scientist and Team Lead, potentially leading to a mismatch in expectations or underutilization of their advanced skills. The lack of specific data analyst tools or domain-specific project details (beyond AI/ML modeling) makes it harder to assess direct cultural fit for a pure data analyst role.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership and collaboration skills through their roles as team leader and manager in various projects and companies. Their research background suggests strong problem-solving and analytical capabilities. The descriptions indicate an ability to work in complex, real-world environments and manage teams effectively.