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Experiencia
- Zurich InsuranceGenAI Tech LeadHIGH TECHseptiembre de 2024 - Hoy (1 año y 11 meses)SpainLead a team of up to 10 engineers across two document understanding projects using LLMs. Own technical strategy, architecture, and delivery for business-critical data extraction systems.Policy Document Intelligence– Designed and deployed LLM extraction pipeline processing 5,000–10,000 policy documents/month, extracting 200–500 structured variables per document– Replaced legacy manual systems for detecting discrepancies between master and local policies, eliminating coverage misalignment that previously required full manual review– Transformed unstructured policy data into structured KPI feeds for business analytics and compliance monitoring Clause Detection & Language Quality– Built system to identify specific legal clauses and extract relevant text snippets from policy documents– Developed quality scoring framework assessing language clarity and regulatory complianceTechnical leadership product development
- LiveWell by Zurich — Zurich InsuranceTech LeadHIGH TECHmayo de 2022 - enero de 2024 (1 año y 8 meses)SpainPromoted from Senior DS to Tech Lead. Led teams of 3–5 building AI-powered wellbeing features for a consumer health platform serving thousands of monthly active users.– Designed and shipped Generative AI content feature delivering personalized wellbeing recommendations– Built NLP validation layer (TF-IDF, embeddings) ensuring LLM output quality before user-facing delivery– Built end-to-end recommendation engine using BERT embeddings for personalized content delivery– Designed DynamoDB schema and serverless API (AppSync + Lambda) powering the recommendation layer– Architected data model on DynamoDB + Redshift Serverless; defined all infrastructure as Terraform IaC with CI/CD on GitHub Actions
- AmazonData Scientist II — EU RME Predictive Analyticsjunio de 2020 - agosto de 2021 (1 año y 2 meses)RemoteBuilt predictive maintenance and NLP systems for Amazon's Reliability & Maintenance Engineering team, analyzing data across 50–60 European fulfillment centers.– Designed predictive maintenance models (XGBoost) for industrial machinery to reduce unplanned downtime across EU operations– Built NLP system to detect temporal patterns in maintenance work orders, improving scheduling efficiency– Conducted exploratory analysis on terabyte-scale datasets spanning the majority of Amazon's EU fulfillment network– Developed NLP tools for automated data quality audits of maintenance work orders
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Formación
- Master — Big Data and AnalyticsMBIT School2019Master — Big Data and Analytics
- Bachelor of Aerospace EngineeringUniversidad Politécnica de Madrid (UPM)2017Bachelor of Aerospace Engineering