Nicole Zattarin

About Me

Quantitative finance professional with a background in physics and deep expertise in machine learning, data science, and financial modeling. As Lead Quant Analyst at XSOR Capital, an emerging fund, I lead the design and implementation of algorithmic trading systems and scalable trading infrastructure to support high-performance investment strategies.

My expertise spans quantitative research, systematic trading, and low-latency execution systems, with strong technical skills in C++, Python, and modern data science technologies. I bridge research, trading, and operations to ensure quantitative models are production-ready and aligned with investment objectives.

Algorithmic Trading

Physics

Artificial
Intelligence

Deep
Learning

Work Experience

XSOR Capital July 2022 - Present

Lead Quantitative Analyst - London, UK
• Lead full-cycle development of systematic FX trading strategies, spanning quantitative research, data analysis, model validation, and live market deployment.
• Architected low-latency C++ trading infrastructure with FIX protocol connectivity, execution algorithms, and automated risk management to support global currency markets.
• Designed robust backtesting frameworks for scenario simulation and investment decision analysis under extreme market conditions.
• Performed feature engineering on high-frequency Limit Order Book (L2) data to derive microstructure-based signals for data-driven strategy evaluation.
• Collaborated with portfolio management and leadership on the fund's technology roadmap and infrastructure design.

Smart KYC Feb 2023 - May 2023

NLP Engineer Intern - London, UK (Remote)
• Designed and deployed an LSTM-based NLP model achieving 93% accuracy across 18 categories for automated KYC compliance and name-entity recognition.
• Built a semi-automated data acquisition and cleaning pipeline integrating web scraping and NLP preprocessing, cutting manual review time by 70%.

Education

MSc Data Science and ICT 2021 - 2023

University of Padua - GPA: 110/110 cum laude
• Machine Learning, Deep Learning, Neural Networks
• Big Data Management (Spark, PostgreSQL)
• AI Applications (NLP, Computer Vision)
• Thesis: Developed a deep learning system for automated Customer Due Diligence (AML/KYC) using name-origin classification to enhance compliance efficiency and reduce operational risk.

BSc Physics 2018 - 2021

University of Milan - GPA: 110/110 cum laude
• Statistical Physics, Advanced Calculus, Data Analysis
• Algorithms and Data Structures
• Quantum Computing
• Research: Conducted simulations of quantum systems and explored quantum machine learning methods for data-driven pattern recognition.