Hire the Best Classification Specialists
in France

As a French Quantitative Algorithmic Trader with dual MSc degrees (Quantitative Finance; Mathematics Applied to ML/DL) and 4+ years of experience, I specialize in building and optimizing end-to-end algorithmic trading systems with a strong focus on backtest quality, strategy design, and deployment readiness. I excel in translating advanced Quantitative Research and Trading Strategies into production-grade Python trading pipelines that deliver consistent, risk-adjusted returns across different asset classes. Core Expertise: Robust Backtesting & Validation - Designed rigorous k-fold walk-forward validation protocols to ensure model generalization and robustness. - Specialized in high-fidelity backtesting frameworks (5+ years of tick/minute data) with transaction costs, slippage, and execution modeling. - Optimised take profits, stop losses and size to improve returns, Sharpe Ratio and Calmar Ratio. Algorithmic Trading Systems in Python : - Proficient in integrating ML/DL models, RL agents, and signal ensembles into live-ready trading codebases. Strategy Design & Innovation - Experienced in alpha factor research, systematic strategy design, and multi-horizon forecasting. - Skilled at combining quantitative finance models (Heston, SABR, volatility surfaces) with modern ML/DL architectures (Transformers, GNNs, RL). Machine Learning & Reinforcement Learning for Trading : -Built RL-based trading agents (PPO, DQ-Net + TFT) achieving Sharpe 1.95 and 20% alpha on BTC spot forecasting. - Developed multi-model ensembles (TFT + Graph Neural Networks) for volatility and alpha prediction. Data Engineering & Feature Integration - Engineered complex feature sets from OHLCV, on-chain metrics, sentiment data (Twitter, Reddit, News), and cross-asset signals. Key Project Highlights: -BTC Perpetual Futures – Live Algo: Designed and deployed a Python strategy achieving Sharpe 1.6 and Calmar 1.6 after costs, with 15% outperformance vs benchmark returns over live trading horizon. - SPY Futures – Multi-TF FVG Strategy: Built a multi-timeframe Fair Value Gap model with positive Sharpe/Calmar, demonstrating robustness and adaptability to equity index futures. -Developed RL agents ( DQ-Net with TFT) with Sharpe 1.95 Calmar 1.6 and 20% alpha in crypto spot forecasting. Technical Toolkit: Languages: Python ML Frameworks: PyTorch, TensorFlow, scikit-learn, Optuna, Vectorbtpro, RLTrading Data Tools: pandas, numpy Domains: Equity index, Crypto/Altcoin Markets, Time-Series Forecasting, RL, Risk Management Education: - MSc Quantitative Finance (Paris Saclay) - MSc Mathematics Applied to Machine Learning & Deep Learning (ENSIIE)

Ex-CERN, STMicroelectronics and Atos AI Engineer. I build Automation systems and AI Apps that run reliably in production. My background—LLM systems at CERN, on-device AI at STMicro, Full Stack Apps at Atos—means I understand what's happening underneath, which is what separates a demo from a system that doesn't break the first time a user does something unexpected. I bring that same rigor to client work: systems that are reliable, measurable, and maintainable. 🛠 What I build: ✅ Workflow Automation (n8n / Make / Zapier): CRM syncing, lead routing, invoice processing, report generation, multi-app integrations — I connect your tools so data moves without anyone touching it manually. ✅ Airtable Systems: Certified Airtable Builder + AI App Builder. I design Airtable bases as lightweight internal tools — client portals, project trackers, inventory systems — often paired with automations that keep them updated automatically. ✅ AI Agents & Chatbots: Practical AI agents (LangChain/LangGraph) wired into your existing stack — support bots, data-extraction agents, RAG, internal assistants that plug into Slack, email, or your CRM. ✅ API Integrations & Custom Backends: When no-code hits a wall, I build the missing piece with Python/FastAPI so the automation keeps running. ✅ Also available for: Computer vision, on-device/edge AI, and Flutter mobile integration, for clients who need deeper technical work. Stack: n8n • Make • Airtable • Zapier • Python • FastAPI • LangChain • LangGraph • OpenAI • Claude • Gemini • React • Supabase • MongoDB 🇬🇧 English Fluent 🇫🇷 Native French speaker. Message me with what you’re building—I’ll tell you honestly if I’m the right fit and how I’d approach it. Cheers, Marouan P.S - if you work with a niche or subject area that I didn't directly list above, I would still encourage you to reach out. I enjoy immersing myself in new subject areas and improving my knowledge; more than willing to be flexible, given that it’s an investment that makes sense for the both of us.

AI Data Improvement Specialist (primarily in French), I work on MTPE (EN→FR), as well as verification and evaluation of text, audio, image, and video data for AI. I also perform UX/UI testing and bug detection. With over 15 years of experience in electrical engineering, I bring rigor, analytical skills, and reliability to every project. Spécialiste de l’amélioration des données IA (principalement en français), je travaille sur la post-édition MTPE (EN→FR), la vérification et l’évaluation de données textes, audio, images ou vidéos pour l’IA. Je pratique également le test UX/UI et la détection de bugs. Fort de plus de 15 ans d’expérience dans l’ingénierie électrique, j’apporte rigueur, esprit analytique et fiabilité à chaque projet.

I sit at a crossroads that combines professional sound engineering, formal musicology training, and systems-level data organization. That combination is what makes me useful to music tech companies, AI audio teams, and labels dealing with catalog chaos. I cover the full chain — hearing whether an AI voice is natural, understanding why a metadata field matters, and building the pipeline to keep it organized automatically — without handoffs. AI Audio & Music Evaluation Perceptual assessment of voice, music, and generative audio: naturalness, emotional accuracy, rhythmic precision. I produce structured, reproducible evaluation rubrics, not vague impressions. Formal musicology training plus sound engineering background means ears that are both analytically trained and technically calibrated. Native trilingual evaluator: Arabic · French · English. Metadata & Catalog Management I design classification systems, write indexing rules, and enforce them with the same rigor I applied as a digitization archivist under pharmaceutical quality control protocols. Familiar with ISRC, UPC, and DDEX logic. I build the taxonomy before touching a single file. Market & Artist Intelligence I track competitor activity, chart movements, and industry trends using Viberate, Soundcharts, and Chartmetric. I design monitoring systems that surface what matters daily, automatically. Workflow Automation & Data Make, Zapier, Notion, Airtable, Google Workspace for pipeline automation. Advanced Excel and Google Sheets. Working knowledge of SQL and Python, actively applied to data cleaning, querying, and reporting workflows. Notion as a relational database with full metadata architecture. Most music metadata freelancers are either technical without the ear, or musical without the systems thinking. I hold two separate bachelor's degrees — Sound Engineering and Musicology — because that's genuinely how I think: structure and art, always simultaneously. Core Skills AI Audio Evaluation · RLHF Feedback · Music Metadata · Catalog Auditing · ISRC / UPC / DDEX · Make · Zapier · Notion · Airtable · SQL · Python · Excel · Viberate · Soundcharts · Chartmetric · Arabic · French · English

I work at the intersection of language, meaning, and communication, across both human-facing content and AI systems. I’m a language specialist with a strong focus on French language quality, nuance, and clarity, combining editorial precision with an understanding of how language functions in technical, marketing, and AI-driven contexts. My background is rooted in multilingual environments, creative writing, and rigorous language standards. I work on content that needs to be accurate, natural, and aligned with intent, whether it’s written for readers, users, or AI models. I have hands-on experience with: • High-quality language data for AI and LLMs • Conversational data creation and semantic analysis • NLP labeling, intent classification, and tone alignment • AI response evaluation, error detection, and hallucination spotting • Editing, proofreading, and content refinement in French • Marketing content, long-form articles, and SEO-oriented content • Email campaigns and brand messaging • Translation and MTPE (English ↔ French) • Linguistic consistency, terminology management, and style adherence I’m particularly strong at capturing linguistic nuance and ensuring that French sounds natural, precise, and human, while strictly respecting guidelines, taxonomies, or editorial frameworks when required. Languages are not just tools to me. They are systems of meaning, culture, and context. That’s why my work focuses on depth, coherence, and semantic accuracy rather than surface-level wording. Beyond professional language work, I’m also an author, with a book published in France. I write personal and narrative texts and understand how to structure ideas clearly, whether for editorial content, marketing materials, or long-form writing. Languages: • French: native • English: fluent • Moroccan Arabic (Darija): fluent • German: basic • Portuguese: basic What I can help you with: • AI / LLM language annotation and evaluation • Conversational and semantic data creation • MTPE and professional translation (EN ↔ FR) • Editorial editing and proofreading • Daily social media account management • Marketing and SEO content • Email campaigns and brand messaging • Multilingual language quality and consistency

I have hands-on experience working with large-scale datasets and building data processing workflows for machine learning tasks. At Scale AI and Innodata, I worked with both text and image data, handling high volumes (5,000+ samples daily). My responsibilities included data processing, classification, semantic analysis, and validation, with a strong focus on data quality and consistency. Over time, I moved beyond manual data work and started improving workflows by introducing automation. I used SQL for data filtering and validation, and tools like n8n and Azure to build more efficient data pipelines. This helped reduce manual effort, improve consistency, and increase processing speed. I also have practical experience working with LLM tools such as ChatGPT and Claude. I used them for tasks like semantic validation, classification, and assisting in workflow automation. I focus on using these tools as part of structured processes, rather than standalone usage. Core skills: Data processing and validation (text & image) SQL (data filtering, querying, validation) Workflow automation (n8n,Zapier) LLM tools (ChatGPT, Claude) Basic Python Azure (data pipelines and storage)