Partnering with global clients across diverse industries to reframe messy business challenges as structured analytical problems, then building the models that solve them. Working across the full stack — from scoping and prototyping to deploying scalable ML systems that drive measurable operational change.

Embedded in cross-functional teams with data engineers, ML engineers, and industry experts. Presenting data-driven insights directly to executive stakeholders, ensuring every AI initiative connects to concrete strategic value.

Led teams of 3+ data scientists and engineers across the complete project lifecycle — client pitching, solution design, build, and post-deployment support. Delivered custom agents and AI solutions for clients ranging from local enterprises to major international corporations in Energy and Retail.

Designed generative AI tools and predictive models that improved operational efficiency and executive decision-making. Ran internal mentorship sessions to level up junior developers in model validation and client communication.

Built high-accuracy time-series forecasting models for service-sector clients with direct bottom-line impact. Owned the technical execution end-to-end — translating abstract business goals into concrete data science roadmaps, then delivering them within the regulatory and data constraints of Energy and Retail.

Researched time-series classification for clinical applications, building preprocessing pipelines for noisy real-world data. Worked alongside researchers and medical staff to interpret model outputs and shape the study's final conclusions.

Bilingual program in English and Spanish, including an Erasmus semester in Warsaw, Poland. Winner of the Datathon at Madrid Engineering Week.