Quantitative Finance · Data · Software
01 · About
I’m Raphaël Catenacci, an engineering student at ENSAE Paris, part of the Institut Polytechnique de Paris, graduating in 2027. I am currently completing the final year of a Master’s degree in Financial Engineering at the Université de Montréal in Canada.
I am passionate about quantitative finance, machine learning, and software development. I build data-driven financial tools, from automated market-data pipelines and portfolio-risk models to pricing engines and machine-learning research workflows.
Before moving into finance and technology, I competed as a high-level BMX Racing athlete, reaching 9th in the U18 UCI World Ranking in 2019. This experience shaped the discipline, resilience, and performance mindset that I bring to every technical and quantitative project.
Download my CV →02 · Background
Exchange / Academic Program · Montréal, Canada
Academic experience in a leading North American research environment, complementing my quantitative training through applied data science, optimization and financial modelling.
Relevant coursework: Stochastic Programming and Dynamic Pricing.
Engineering School · Palaiseau, France
Engineering degree at the Polytechnic Institute of Paris, with a focus on quantitative methods, statistics, financial mathematics and machine learning.
Relevant coursework: Financial Mathematics, Stochastic Processes, Machine Learning, Financial Instruments, Time Series Analysis, Statistical Simulation and Monte Carlo Methods.
03 · Experience
Quantitative Researcher Off-cycle · Monaco, MONACO
Built a fully automated, provider-agnostic financial data infrastructure for scalable market-data ingestion and processing. Developed local-LLM workflows to structure macroeconomic and alternative-data pipelines.
Automated instrument lifecycle management, corporate actions, pricing updates, portfolio synchronization, risk calculations, stress testing and trading-portfolio pricing models over cross-asset and cross-strategies portfolio.
Implemented sampling, meta-labelling and sample-weighting methodologies to improve predictive signal quality before machine-learning deployment.
Python · JavaScript · Bloomberg · Financial ML · Risk Modelling
Data Scientist Intern · Paris, FRANCE
Developed machine-learning algorithms within the Financial Compliance team. Built an artificial neural-network solution using the Transformers library and Azure Cloud services to support financial-data analysis and compliance workflows.
Python · PyTorch · Transformers · Pandas · Scikit-learn · Azure
04 · Expertise
Python, SQL, JavaScript, HTML, CSS, R, Git.
Quantitative research, market data, portfolio analytics, risk, pricing and financial modelling.
Data pipelines, APIs, databases, web applications, automation and machine learning.
Django, Numpy, Pandas, Polars, Scikit-learn, PyTorch, Transformers, CatBoost
05 · Selected work
Quantitative Finance
Designed and implemented multiple ML-driven trading strategies, evaluated with Sharpe ratio, drawdowns, and other risk-adjusted metrics.
Machine Learning
Built a CatBoost model to forecast French national gas consumption, leveraging AutoML and hyperparameter tuning for robust time-series predictions.
06 · My company
RC is a solo-owned technology studio specializing in bespoke software
solutions that make complex workflows simple, reliable, and measurable.
Our mission is to help teams regain time and clarity by designing intelligent data pipelines, automating high-friction processes, and building fully customizable interfaces tailored to real operational needs.
From quantitative research and financial operations to internal tooling and process automation. Our approach combines deep technical expertise with a strong understanding of business and compliance constraints.
End-to-end ingestion, cleaning, enrichment, and orchestration of structured and unstructured data, with robust monitoring and lineage tracking.
Scripted and event-driven automation of repetitive or error-prone processes across desktop, web, and backend systems to reduce manual effort and operational risk.
Integration of local or cloud-based AI models to enable conversational interfaces, document understanding, and multi-agent coordination within your custom software stack.
07 · Services
Financial, economic and alternative data pipelines designed for reliability, scalability and reuse.
Research tools, portfolio analytics, risk calculations, pricing models and backtesting environments.
Tailored web applications, APIs, automation workflows and business tools adapted to your processes.
Reach out to discuss your project and make a tailored reality.
08 · Case study
Financial data & quantitative infrastructure
During my off-cycle experience at Jukoï Capital in Monaco, I worked on the development of a fully automated, provider-agnostic financial data infrastructure designed to ingest and process market data at scale.
The project combined financial data engineering, quantitative research and application development. The infrastructure was designed to support market data ingestion, macroeconomic and alternative data workflows, financial instrument lifecycle management, portfolio synchronization, risk calculations and pricing models.
Local large language models were used to simplify and structure macroeconomic and alternative data workflows, particularly through data cleaning, normalization, field mapping and pattern detection. I also implemented sampling, meta-labeling and sample-weighting methodologies to improve the quality of datasets and maximize predictive signal before machine learning model deployment.
The resulting components were integrated into a web-based financial application, connecting quantitative research workflows with operational portfolio and risk-management tools.
Build a scalable and flexible financial data infrastructure capable of working with multiple data providers while supporting market data, macroeconomic data, alternative data and portfolio processes.
Designed an automated provider-agnostic pipeline, enhanced data workflows with local LLMs, implemented quantitative data-preparation methodologies, and automated instrument lifecycle, risk, pricing and portfolio processes.
Created a reusable foundation for scalable market-data processing and integrated quantitative tools into a web application, improving the consistency and automation of research, trading and portfolio workflows.