Cristina Martínez is a data scientist and demand forecaster with eight years of experience applying statistical modeling, machine learning, and data visualization to optimize pharmaceutical supply chains at Sanofi. She owns end-to-end forecasting processes—from data cleansing and outlier detection to model optimization and KPI-driven performance analysis—while closely integrating local market insights to improve accuracy. Her background in biological sciences and advanced statistics gives her a strong grounding in experimental rigor and applied analytics across R, Python, Databricks, SQL and Snowflake. She has built practical tools including an R Shiny app for standardized product strategy decisions and an image-detection prototype for packaging logo recognition. A regular mentor and communicator, she presents findings to stakeholders and even spoke at Mobile World Congress, underscoring her ability to translate technical work into business impact. Outside of work she shares analyses and code via a personal blog and GitHub, blending open-data curiosity with enterprise-scale forecasting.
8 years of coding experience
3 years of employment as a software developer
CITIUS postgraduate training programme. Fundación Universidad-Empresa, CITIUS postgraduate training programme. Fundación Universidad-Empresa at Universidad Autónoma de Madrid
Degree in Biology, Degree in Biology at UA - Universidad de Alicante / Universitat d'Alacant
Biological Sciences, Biological Sciences at University of East Anglia
Master of Science (M.Sc.) · Big Data Analysis Technologies, Master of Science (M.Sc.) · Big Data Analysis Technologies at Universidad de Murcia
Postgraduate degree, Advanced Methods of Applied Statistics, Postgraduate degree, Advanced Methods of Applied Statistics at Universidad Nacional de Educación a Distancia - U.N.E.D.
Código Python relacionado con los contenidos en el repositorio Ciencia-de-Datos-R
Contributions:4 PRs, 14 pushes, 6 branches in 1 year 3 months
pythondata-sciencerepositoriocondatos
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Cristina Martínez - Demand Forecaster Data Science