Description

Hola, this is Leonardo

🚀 I help companies build and deploy production-ready AI systems that unlock the full potential of their data.

🔹 Full-Stack Expertise: I combine Data Science & Data Engineering skills, leading teams with a results-driven mindset to deliver impactful AI solutions.

🔹 End-to-end AI Support: From designing AI strategies, ETL pipelining, ML modeling, and evaluation to deploying ML models at scale.

🔹 LLM & NLP Focus: Currently specializing in Large Language Models (LLMs) to tackle real-world NLP challenges. Check out my Medium blog articles @lgsquare

🎯 With 10+ years of experience across Europe, the US, and LATAM, I’ve helped businesses in finance, microelectronics, energy, automotive, research, and education harness AI to grow their revenue.

📚 I’ve authored/co-authored multiple AI & ML papers with 100+ citations in peer-reviewed journals and conferences, where I’ve also been a speaker.

By working with me, you can trust that I will deliver a high-quality solution while ensuring alignment with your business constraints.

Feel free to reach out if you have any questions about my expertise or experience.

Hasta pronto!

Industry field of expertise

Languages

French
Native or bilingual
English
Fluent
Spanish
Native or bilingual

Workplace preferences

Can work on-site

Bruxelles (up to 50km), Namur (up to 10km), Liège (up to 10km), Anvers (up to 10km), Louvain-la-Neuve (up to 10km)

BNP PARIBAS
Senior Data Scientist
February 2024 - January 2025 (11 months)
Luxembourg, Luxembourg
Supporting the DataLab team in developing AI solutions in the banking sector.

- Contributed to the development of Generative AI (GenAI) applications, utilizing advancements in open-source Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures with private banking data.

- Designed and implemented evaluation and monitoring frameworks for GenAI models to ensure reliability, accuracy, and compliance in production environments.

- Developed and optimized ETL pipelines to streamline transaction data flows, enabling the creation of effective machine-learning models for fraud detection in AML/KYC use cases.

- Led the RD&I workstream to develop cutting-edge solutions in ML monitoring (drift detection) in collaboration with UniLux (SNT) researchers.

- Supervised the development of Recommender Systems to enhance marketing strategies and drive customer engagement.
Goodyear
Data Scientist, Principal Investigator
November 2021 - December 2023 (2 years and 1 month)
Luxembourg, Luxembourg
Conducting applied research in the Data-Driven Engineering team, providing cutting-edge solutions for the automotive industry with applications in virtual tire design and tire intelligence products.

- Responsible for the planning, coordination, execution, deployment, and monitoring of data-driven engineering projects involving a cross-functional global team from the USA and Europe.

- Developing ML models to support advanced tire simulation capabilities leveraging multi-fidelity data.

- Creation of novel algorithms combining multi-performance optimization, FEA simulations, and ML to support the virtual tire design process.

- Developing ETL pipelines to process semi-structured data by ingesting millions of records from multiple sources to the cloud data lake storage within a CI/CD process.

- Exploiting large tire sensor data to build and deploy ML models via REST-API systems.

- Communication of project status, milestones, and challenges to team members, managers, and senior directors.
LuxembourgInstitute of Scienceand Technology
Data Scientist, Applied Researcher
November 2019 - November 2021 (2 years)
Esch-sur-Alzette, Luxembourg
Performing applied research to develop tools and solutions in the automotive industry, leveraging data mining, machine learning and optimization at Goodyear and LIST partnership.

- Providing input to the definition of research concepts in collaboration with LIST and Goodyear.

- Designing and developing a similarity algorithm to compare high-dimensional tabular data
containing mixed-type (numerical and categorical) variables.

- Developing kernel-based ML algorithms to predict multiple tire-related performances.

- Developing a multi-performance optimization engine leveraging data-driven objectives and
physical constraints.

- Contributing to knowledge generation and dissemination of results as technical reports, research
papers, presentations, and software packages.