PROFILE / FIELD NOTES
About
I am a data and AI consultant, agentic entrepreneur, and founder based in Paris. I ship AI products through LumiereAI, and my experience spans production data science at Decathlon and COMPREDICT, data science and data engineering at Alstom, research engineering at VibraTec, and engineering education at INSA Lyon and Mines Rabat.
Current focus
- Agentic systems
- Retrieval
- Time series
- Practical AI tools
Technical strengths
- Python
- Machine learning and deep learning
- NLP and LLMs
- Time series
- Signal processing
- Apache Spark
- AWS and Databricks
- MLflow
- Data visualization
Experience
- Co-Founder · LumiereAI ·
- Designed, built and shipped a multi-stage LLM pipeline in production, with every model call site behind one provider-agnostic protocol, a deterministic floor that guarantees delivery when the model pass fails, and a measurement harness that cut end-to-end wall clock 49%, serving users at pipelinevideo.com.
- Designed, built and shipped a multi-stage agentic RAG pipeline in production, with a provider-agnostic model layer behind one environment switch, a deterministic floor that downgrades ungrounded output to not verified rather than guessing, and criterion-level batching that cut 32 sequential LLM calls to about 7 concurrent ones, serving users at auditmalin.com.
- Built a multi-layer image-forensics engine combining seven classical and deep-learning signals with an LLM judge under a strict structured-output schema, benchmarked at 80% accuracy with 100% precision on hard negatives, recall improved by about 80% relative through threshold recalibration, and awarded two prizes including best overall at the Pioneers hackathon at Station F, live at fidelityai.dev.
Tech:
- Senior Data Scientist · Decathlon ·
- Designed and productionized a modeling approach for heterogeneous SKU and store demand series, enabling cross-series learning across about 280k SKUs and 2.4k stores, 69M listed SKU and store pairs and more than 2B rows of history, forecast weekly at an 8-week horizon.
- Spearheaded SHAP-based explainability for production demand forecasting, building reusable global and SKU-store diagnostic workflows, mentoring teammates, and using feature attribution to uncover model behavior and upstream data-quality issues.
- Redesigned forecasting evaluation beyond WAPE and aggregate bias with naive-baseline signal, scaled MASE and RMSSE metrics, relative error, and directional SignedBias across dbt and Spark monitoring pipelines, so over-forecasting, under-forecasting, volume, and turnover impact can be judged separately.
- Led end-to-end feature engineering over weather, price, sales-derived, store and item attributes, from source-data analysis and feature design through ELT pipelines to model integration, backtesting, monitoring, and stakeholder communication, across multiple markets, product universes, and forecast cutoffs.
- Pioneered internal evaluation of tabular and time-series foundation models, extending the experimentation platform for TabPFN and TabICL and assessing accuracy, interpretability, data requirements, and GPU scalability constraints.
- Built an agent-ready data-science monorepo using reusable agent skills, MCP connectors, and operational documentation, enabling agent-assisted end-to-end experimentation and a reported 2x acceleration in research and development throughput.
- Designed a RAG system over multilingual product descriptions: an LLM returns a fixed list of product attributes, retrieval finds the past products a new item most resembles and the product it replaces, and the same text embeddings become features a tabular foundation model can learn from, which is what a launch forecast has to lean on when the item has no sales history of its own.
- Designed an assistant for forecast support that reads an incoming ticket, gathers the forecast, sales, stock and metric evidence through read-only tools, finds similar cases already resolved, and drafts an answer in which every figure names the query it came from, for a human to approve before it is sent, removing about 7 hours of manual engineering support a week.
Tech:
- Senior Data Scientist · COMPREDICT ·
- Created a novel time series model, FFD, that reduced the company's previous state-of-the-art model size by about 50 times while keeping more than 97% of its predictive accuracy.
- Built and maintained the internal libraries that kept models deployable to production.
- Reduced and optimized model sizes for in-vehicle deployment through distillation, quantization, and pruning.
Tech:
- Data Scientist · Alstom ·
- Developed and deployed the company's first deep learning models for time series, predicting train responses to environmental and architectural changes in the railway industry.
- Led data gathering and custom preprocessing across a team, cutting simulation time and reliance on expensive multibody simulations by 60%.
- Deployed machine learning models that estimate the cost of tender tasks from historical data and return of experience, which cut the time each tender took.
- Deployed transformer-based NLP models that unified disparate component classifications, standardizing more than 2,500 parts for downstream predictive maintenance.
- Data Engineer · Alstom ·
- Led statistical and machine learning analyses that informed key design decisions from a railway-dynamics point of view, acting as a point of contact for high-profile clients.
- Built and deployed a web app for designing suspension components from theoretical and empirical data, using machine learning and signal processing.
- Implemented a model predicting successful wheel and axle mounting operations, raising the success rate by 20%.
- R&D Engineer · VibraTec ·
- Implemented a signal-processing package centered on convolution techniques.
- Developed an app predicting wheel and rail forces and estimating the impact of wear and rolling contact fatigue in the railway industry.
- Co-wrote a paper with LAMCOS at INSA Lyon.
- Technical Project Manager · Mines Rabat Solar Team ·
- Modelled, designed and built a solar-powered car with the student team.
- Directed the mechanical team through the structural design of the car.
- Won the Moroccan Solar Race Challenge and took part in COP22.
Languages & interests
Languages
- French (Native or bilingual)
- Arabic (Native or bilingual)
- English (TOEIC 975/990)
- German (Elementary)
- Spanish (Elementary)
Interests
- Music (Guitar)
- Sports (Rowing, Running and Swimming)
Certifications & awards
Certifications
- Machine Learning Specialization (DeepLearning.AI and Stanford Online)
- Deep Learning Specialization (DeepLearning.AI)
Awards
- Patent for an innovative suspension system
- Winner, best overall, Paris AI Hackathon by Pioneers at Station F, 2025
- Innovation prize
- Winner, national debating competition
- Semifinalist, The Great Debaters national competition
Education
- Engineering degree, Computer Engineering · INSA Lyon ·
- Master of Engineering · Mines Rabat ·
- MP - MPSI · CPGE Taza ·