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Data science | Python | Github | Analytics | Big Data | Machine learning | Deep learning | Digital marketing | Statistical analysis | Google Cloud Platform | Cloud computing | Agile Scrum Data Scientist with experience across digital marketing, retail, e-commerce, and B2B/B2C environments. I focus on using data to drive growth, optimize performance, and support data-driven decision-making. Científica de Datos con 6+ años de experiencia en analítica avanzada para crecimiento y marketing en entornos B2C y B2B, trabajando con bases de millones de usuarios. Fuerte formación en Estadística (pregrado y maestría) y experiencia práctica en experimentación a gran escala, medición causal en entornos productivos, y modelos de propensión/uplift para optimizar targeting y performance. Capacidad para defender enfoques y resultados ante áreas financieras y traducir hallazgos estadísticos a impacto económico y decisiones ejecutivas. Competencias clave: * Experimentación y medición causal: A/B testing avanzado, uplift de conversión, intervalos de confianza, validación de impacto y control de sesgos. * Modelos de propensity & churn (retención): propensity scoring (conversión/compra), modelos de churn, segmentación accionable, priorización de audiencias y evaluación de incrementalidad. * ML y estadística aplicada: modelos supervisados (clasificación) y no supervisados, series de tiempo/forecasting y modelamiento estadístico. * Marketing analytics: MMM, atribución, performance y optimización de inversión (ROI/ROAS). * NLP/LLMs: soluciones con LLMs (integración con Gemini) para casos de negocio. * Stack: Python, SQL, R, Git/GitHub, GCP (BigQuery, Vertex AI), ETL/pipelines, Databricks, MLflow y dashboards/BI. Certified data scientist by correlation-one, DS4A. 2021 Cohort. Github profile: https://github.com/juli4888
Led end-to-end data analysis and modeling initiatives focused on marketing performance, customer engagement, and email deliverability across multiple clients. Built analytical frameworks, automated pipelines, and dashboards to monitor key KPIs and support data-driven decisions. Translated complex analyses into actionable business insights and collaborated with cross-functional teams to deliver scalable, high-quality solutions.
At Havas, I led the development of Marketing Mix Modeling (MMM) solutions to analyze the impact of digital and traditional media investment on revenue, ROI, and ROAS across clients in Latin America and Europe. I also built an LLM-based sentiment analysis solution to recalculate NPS from customer reviews for a major telecommunications client. Additionally, I developed time series models for KPI anomaly detection, enabling early identification of irregular patterns in digital performance.
Developed machine learning–based audience builder solutions to identify high-value audiences based on consumer interests and affinities. Applied supervised models for customer segmentation and retention, while optimizing and refactoring Python code to improve process efficiency and scalability.