Pietro Patelli

[email protected] · pietropatelli · pietropatelli

Passionate about Economics, Finance and Data Science

Skills

programming object-oriented programming data science economics finance market data econometrics big data tools networks llms machine learning sentiment analysis databases linux

Programming: Python, SQL, R, MATLAB, Bash, Vimscript, LaTeX, HTML, CSS

Tools: Git, Github, Docker, Hadoop, Spark, CDSW, STATA, Bloomberg, Refinitiv, Tableau, MS Office Suite

Languages

English: C2, main working language · Italian: Native · Spanish: B2 · German: B2

Experience

Bank for International Settlements & Financial Stability Board

Senior Data Scientist

2024–present, Basel, Switzerland

• I contribute to reports, monitoring exercises and publications by the FSB, including the flagship global monitoring exercise on non-bank financial intermediation.
• I work across a diverse range of topics, including cryptoassets, term premia modelling, and quantitative systemic risk indicators.
• I am also the subject-matter expert for key specialised datasets, including on money market funds, derivatives margining, and fixed income instruments.

Technologies used: Python; SQL; MATLAB; Tableau; Git; Github; Copilot.

Bank for International Settlements

Financial Market Analyst, International Assignment

2023–2024, Hong Kong, Hong Kong SAR

• I worked on policy notes, monitoring exercises and research pieces with a particular focus on Asia Pacific and Emerging Market Economies.
• I contributed to regular monitoring meetings for the region, both directly covering countries and producing broader analyses.

Technologies used: MATLAB; Python; STATA; Gephi.

Financial Market Analyst

2021–2024, Basel, Switzerland

• I worked on a varied range of research projects, from standard econometric analysis to machine learning, sentiment analysis, and market indicator development.
• I worked on one of the flagship policy publications, the Quarterly Review, as well as several other policy pieces.
• I was responsible, on the Analysts’ side, for the main internal regular monitoring exercise, as well as some key event-driven monitoring exercises.
• I was responsible for several specialised datasets, and the onboarding of some new data sources.
• I contributed to the onboarding of new technologies and the development of new processes, as well as designing several new internal tools in collaboration with IT.
• I produced newsletters and other material circulated to internal and external stakeholders.

Technologies used: Python; MATLAB; Bloomberg; Refinitiv; SQL; Tableau; Git; Azure DevOps; Github.

European Central Bank

Data Science Analyst

2021–2021, Frankfurt am Main, Germany

• I was part of the Derivatives Team, managing a large-scale dataset of derivatives transactions reported under EMIR, using Python, SQL and big data tools.
• I analysed derivatives transaction data to identify data-quality and reporting issues, and conducted event-driven analyses of counterparty exposures during episodes of market stress to assess systemic risk.
• I produced newsletters and analyses disseminated to internal and external stakeholders.
• I designed and developed a Python library (class-based architecture, modular and extensible) to automate recurring analytics and handle data-quality issues the existing system could not address.

Technologies used: Python; SQL; Hadoop; Spark; Oracle; Git; Bitbucket.

Statistics - Data Science Trainee

2020–2021, Frankfurt am Main, Germany

• I extended monitoring tools for the collection of data and the daily production.
• I contributed to communication with internal and external stakeholders.
• I managed accesses to the dataset and provided support to users of the data.

Technologies used: Python; SQL; Hadoop; Spark; Oracle; Git; Bitbucket.

Universitat Pompeu Fabra

Research & Teaching Assistant

2018–2019, Barcelona, Spain

• I worked on empirical projects involving diverse datasets, from newspaper text data to geographic electoral data.
• I built an algorithm in Python for the digitalization of a particular form of text data using NLP techniques.
• I performed web scraping and text processing in Python and preparatory data analysis.
• I taught seminars for “International Financial Economics” and “Introduction to Macroeconomics”.

Technologies used: Python; STATA.

IESE Business School

Research Assistant

2017–2018, Barcelona, Spain

• I was involved in a number of research projects, ranging from an empirical study of income shocks to work on the regulatory treatment of NPLs across the Eurozone.
• I contributed to data cleaning, data visualization and literature reviews.

Technologies used: Python; R; STATA.

Projects

SDMX-dashboard-generator: An open-source Dash application that generates dynamic dashboards by pulling data and metadata from SDMX Rest API. Originally developed for the SDMX Hackathon Global Conference 2023.

Education

MRes Economics, Finance and Management

2017–2019, Universitat Pompeu Fabra Barcelona, Spain

MSc Economics and Finance

2016–2017, Barcelona School of Economics Barcelona, Spain

BA (Hons) Economics

2013–2016, University of Cambridge Cambridge, UK

Courses and Certifications

CFA Institute Level II candidate. 2027

University of Edinburgh Summer School Market Microstructure 2025 Edinburgh, UK

Dartmouth College and IMT C Programming with Linux 2025, Online via edX

Euromoney Derivatives. 2023 London, UK

London School of Economics Tools for Macroeconomists: Advanced Tools. 2019 London, UK

London School of Economics Tools for Macroeconomists: The Essentials. 2019 London, UK

Awards & Recognition

2023 SDMX Hackathon Co-Winners

Team Math Olympiads Top three national placement twice

MATEprism math games National second place

Publications

Patelli, P., Shek, J., & Shim, I. (2023). Lessons from recent experiences on exchange rates, capital flows and financial conditions in EMEs (No. 79). Bank for International Settlements

Gelos, G., Patelli, P., & Shim, I. (2024). The US dollar and capital flows to EMEs. BIS Quarterly Review, 51-67.

Gambacorta, L., Kwon, B., Park, T., Patelli, P., & Zhu, S. (2026). CB-LMs: language models for central banking. Journal of Financial Stability, 101585.