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.

PythonSQLAlchemySQLitepandaspytestyfinanceDesktop 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.

PyQt6pandasNumPySciPyPlotlyyfinanceSQLitefpdf2

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).

PythonRuffBlackMypypre-commitGitHub Actionssemantic-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++17Standard LibraryBlack-ScholesMonte CarloCRR

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 16React 19TypeScriptSupabasePostgreSQLTailwind CSSVitestVercel

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 16React 19TypeScriptTailwind CSSVitestIndexedDBYAMLESLint

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-learnpandasNumPyMatplotlib

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.

StreamlitpandasyfinanceMatplotlib

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.