I study financial engineering at ESILV Paris, with a strong interest in investment analysis, portfolio construction and decision-making systems for investors.

I build production-grade financial tools: a French small-cap screener designed for buy-side research workflows, a multi-asset wealth tracker with Monte Carlo projections and efficient frontier analysis, a finance learning platform for interview preparation, a C++ derivatives pricing engine covering Black-Scholes, Cox-Ross-Rubinstein and Monte Carlo simulation, and full-stack applications spanning desktop tools and modern web front-ends.

I believe the most valuable finance skills today sit at the intersection of markets, software and rigorous investment thinking — and I build accordingly.

Currently seeking an internship in investment management, asset management, private equity, family office or fintech.

• Programme — Financial Engineering (Ingénierie Financière), ESILV Paris — Grande École
• Founder — Occifloc · Family business managing B2B textile customisation projects, supplier relationships and business development
• Languages & Certifications — French (native) · English (professional) · AMF Certification
• Location — Paris, France

small-cap-screener · Python / Desktop Research Platform

Institutional-grade screening platform for French small caps (Euronext Paris + Growth), designed for buy-side research workflows. Includes financial normalization pipelines, KPI engine, scoring models, ranking systems, analyst watchlists, exclusions, investment notes and exportable screening snapshots. Built with strict UI → Services → Repositories architecture.

Python SQLAlchemy SQLite pandas pytest yfinance Desktop UI

portfolio-tracker · Python / PyQt6

Consolidated wealth management application covering equities, real estate, private equity, savings and credit. Features Monte Carlo simulations and stress testing, efficient frontier analysis, multi-currency FX-adjusted positions, Sankey cash flow diagrams, and PDF wealth report exports. Optional remote sync via Turso/libSQL. 211 automated tests.

PyQt6 pandas NumPy SciPy Plotly yfinance SQLite fpdf2

github-project-template · Python / Engineering Workflow

Professional Python project template designed for production-grade development. Includes strict architecture enforcement, Conventional Commits, semantic releases, Ruff, Black, Mypy, pre-commit hooks, GitHub Actions CI/CD and dedicated instructions for AI coding agents (Codex, Claude, Cursor).

Python Ruff Black Mypy pre-commit GitHub Actions semantic-release

options-pricing-cpp · C++17

Derivatives pricing library implementing Black-Scholes closed-form, Monte Carlo simulation (with 95% confidence intervals) and Cox-Ross-Rubinstein binomial tree with early exercise detection. Supports European vanilla, European digital, American and Asian arithmetic options.

C++17 Standard Library Black-Scholes Monte Carlo CRR

SportTrack · Next.js 16 / Supabase

Multi-user endurance training platform for athletes and coaches. Includes Strava OAuth sync, ATL/CTL/TSB training load analytics, ACWR injury-risk monitoring, HR zones, qualitative post-session logging, group coaching, planning calendars, Supabase Auth/RLS and Vercel Cron jobs.

Next.js 16 React 19 TypeScript Supabase PostgreSQL Tailwind CSS Vitest Vercel

finance-app · Next.js 16 / TypeScript

Gamified, offline-first finance interview learning app. Uses spaced repetition, progressive unlocks, XP and career-level progression, daily review queues, lesson stars, local progress storage, and an admin interface for managing cards, imports, exports and user suggestions.

Next.js 16 React 19 TypeScript Tailwind CSS Vitest IndexedDB YAML ESLint

ml-portfolio-optimization · Python / scikit-learn

Academic project comparing classical and ML-based portfolio allocation strategies on S&P 500 data. Benchmarks Equal-Weight, Markowitz MVP, Random Forest and Logistic Regression approaches using Sharpe ratio, Calmar ratio and drawdown metrics.

scikit-learn pandas NumPy Matplotlib

Quantitative-Finance-Dashboard · Python / Streamlit

Interactive backtesting platform modelling a quantitative research workflow. Single-asset module: Buy & Hold, Moving Average Crossover and Regime Switching with Sharpe ratio, drawdown and annualised return metrics. Multi-asset module: portfolio construction with rebalancing, risk contribution decomposition, correlation and covariance analysis. Includes automated report generation via cron scheduling.

Streamlit pandas yfinance Matplotlib

Languages

Data / Quantitative

Frameworks & Tools

Infrastructure

Investment Management · Asset Management · Small Cap Investing · Portfolio Construction · Private Equity · Family Office · Financial Software · Fintech · Software Engineering · Entrepreneurship

I am particularly interested in roles where investment judgment and analytical rigor meet.

Looking for a 6-month internship starting from September 2026 or early 2027, through September 2027, in:

• Investment management · asset management · multi-family office
• Equity research · buy-side analysis · small/mid-cap investing
• Private equity · venture capital · growth equity
• Fintech · financial software engineering

Open to positions in Paris, London or internationally.