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Freelance Data Scientist with 4 years of experience and a double master's degree (Applied Mathematics & Statistics; Data Science & Business Analysis), specialized in building and deploying production-grade ML and deep learning systems. I've delivered across banking (2 years on real-time fraud detection for the European Payment Initiative — scoring, PySpark on high-volume streams, production monitoring), healthcare (clinical time series, GAN-based synthetic data at a university hospital), and travel (demand forecasting). My core strength is the full lifecycle: modeling (Python, scikit-learn, TensorFlow, PyTorch), industrialization (Docker, MLflow, DVC, Airflow, CI/CD, FastAPI), and cloud deployment (Microsoft Certified Azure AI Engineer; AWS). Currently expanding into LLM applications (LangChain, RAG, agents). Clients value my autonomy, rigor, and clear communication with non-technical stakeholders — I document, report, and deliver models that keep working after I leave. Fluent in French and English, fully set up for remote, available for long-term engagements.…Freelance Data Scientist with 4 years of experience and a double master's degree (Applied Mathematics & Statistics; Data Science & Business Analysis), specialized in building and deploying production-grade ML and deep learning systems. I've delivered across banking (2 years on real-time fraud detection for the European Payment Initiative — scoring, PySpark on high-volume streams, production monitoring), healthcare (clinical time series, GAN-based synthetic data at a university hospital), and travel (demand forecasting). My core strength is the full lifecycle: modeling (Python, scikit-learn, TensorFlow, PyTorch), industrialization (Docker, MLflow, DVC, Airflow, CI/CD, FastAPI), and cloud deployment (Microsoft Certified Azure AI Engineer; AWS). Currently expanding into LLM applications (LangChain, RAG, agents). Clients value my autonomy, rigor, and clear communication with non-technical stakeholders — I document, report, and deliver models that keep working after I leave. Fluent in French and English, fully set up for remote, available for long-term engagements.

BRUNO STORDEUR

Data Scientist, AI Engineer, Data Analyst

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Freelance Data Scientist with 4 years of experience and a double master's degree (Applied Mathematics & Statistics; Data Science & Business Analysis), specialized in building and deploying production-grade ML and deep learning systems. I've delivered across banking (2 years on real-time fraud detection for the European Payment Initiative — scoring, PySpark on high-volume streams, production monitoring), healthcare (clinical time series, GAN-based synthetic data at a university hospital), and travel (demand forecasting). My core strength is the full lifecycle: modeling (Python, scikit-learn, TensorFlow, PyTorch), industrialization (Docker, MLflow, DVC, Airflow, CI/CD, FastAPI), and cloud deployment (Microsoft Certified Azure AI Engineer; AWS). Currently expanding into LLM applications (LangChain, RAG, agents). Clients value my autonomy, rigor, and clear communication with non-technical stakeholders — I document, report, and deliver models that keep working after I leave. Fluent in French and English, fully set up for remote, available for long-term engagements.…Freelance Data Scientist with 4 years of experience and a double master's degree (Applied Mathematics & Statistics; Data Science & Business Analysis), specialized in building and deploying production-grade ML and deep learning systems. I've delivered across banking (2 years on real-time fraud detection for the European Payment Initiative — scoring, PySpark on high-volume streams, production monitoring), healthcare (clinical time series, GAN-based synthetic data at a university hospital), and travel (demand forecasting). My core strength is the full lifecycle: modeling (Python, scikit-learn, TensorFlow, PyTorch), industrialization (Docker, MLflow, DVC, Airflow, CI/CD, FastAPI), and cloud deployment (Microsoft Certified Azure AI Engineer; AWS). Currently expanding into LLM applications (LangChain, RAG, agents). Clients value my autonomy, rigor, and clear communication with non-technical stakeholders — I document, report, and deliver models that keep working after I leave. Fluent in French and English, fully set up for remote, available for long-term engagements.

Available to hire

Freelance Data Scientist with 4 years of experience and a double master’s degree (Applied Mathematics & Statistics; Data Science & Business Analysis), specialized in building and deploying production-grade ML and deep learning systems.

I’ve delivered across banking (2 years on real-time fraud detection for the European Payment Initiative — scoring, PySpark on high-volume streams, production monitoring), healthcare (clinical time series, GAN-based synthetic data at a university hospital), and travel (demand forecasting). My core strength is the full lifecycle: modeling (Python, scikit-learn, TensorFlow, PyTorch), industrialization (Docker, MLflow, DVC, Airflow, CI/CD, FastAPI), and cloud deployment (Microsoft Certified Azure AI Engineer; AWS). Currently expanding into LLM applications (LangChain, RAG, agents).

Clients value my autonomy, rigor, and clear communication with non-technical stakeholders — I document, report, and deliver models that keep working after I leave. Fluent in French and English, fully set up for remote, available for long-term engagements.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

French

Fluent

English

Advanced

Work Experience

Data Scientist — Card Payments at EPI – European Payment Initiative

March 1, 2024 - December 31, 2025

Built and deployed scoring and fraud-detection ML systems for an instant-payment and digital-wallet platform where fraud prevention is critical to user trust. Developed real-time fraud scoring models and designed “cold start” rules for first-time users to reduce false positives. Conducted exploratory analysis of suspicious behaviors across transaction types (P2P, e-/m-commerce). Ran production monitoring and performance tracking with IBM Safer Payments, including dynamic threshold tuning. Implemented scientific monitoring and benchmarking to propose robust methods against evolving fraud patterns.

Data Analyst / Scientist — Risk ORM Division at BNP Paribas

May 1, 2023 - December 31, 2023

Supported operational risk management through historical incident analysis to target risk areas and reduce costs. Structured an internal incident database by risk categories (e.g., fraud, human error) and linked incidents to root causes including processes, systems, and failing business units. Analyzed correlations to detect recurring patterns across risk perimeters. Produced clear visualizations and reporting for non-technical stakeholders and contributed to business review committees by communicating insights and recommendations.

Data Scientist — Clinical Decision Support (applied research) at Brest University Hospital

October 1, 2022 - April 30, 2023

Worked on ML/DL methods for clinical decision support to help treat intracranial hypertension events in patients. Generated synthetic temporal data using a DGAN model to address rarity and imbalance. Defined sequence quality metrics using Dynamic Time Warping to filter usable synthetic sequences. Modeled arterial and intracranial pressure with step-by-step predictive CNN-LSTM networks and classified hypertension episodes using multiple architectures (TCN, CNN, LSTM, Random Forest, etc.). Maintained ongoing scientific monitoring, testing, and benchmarking of state-of-the-art approaches validated with clinicians.

Data Scientist at Digitrips

September 1, 2021 - September 30, 2022

Optimized flight-booking search caching to reduce user latency and cost. Built relevance classifiers for flight searches to improve cache utilization and reduce false negatives. Developed supervised models (logistic regression, Random Forest, XGBoost, MLP) and time-series approaches (ARIMA, SARIMA, LSTM) to forecast bookings and support budget and logistics planning. Owned the full ML pipeline from SQL extraction and data cleaning to feature engineering, training, tuning, evaluation, and knowledge transfer to the product team for continuous model operation.