As the Data Scientist , your responsibilities will include : Role Overview - Lead data science initiatives within the Digital Operations team, driving innovation and operational excellence across Supply Chain, Manufacturing, and Quality. - Manage and mentor citizen data scientists while spearheading complex data science projects from inception to deployment, ensuring continuous alignment with business objectives. Leadership and Project Management - Lead and manage citizen data scientists through all phases of data science projects, providing mentorship and guidance. - Collaborate with cross-functional teams to design, scope, and prioritize data science projects, including defining data collection methods, modeling techniques, and expected outcomes. - Manage data science projects from initiation to completion, continuously revisiting and refining models to ensure they meet evolving business needs. - Develop and maintain governance principles for data science projects, ensuring best practices are followed. Problem Analysis and Project Management - Develop advanced analytics models (predictive, prescriptive, machine learning, simulation, statistical) to address complex business challenges in Supply Chain, Manufacturing, and Quality. - Utilize advanced machine learning and statistical techniques to perform data discovery, uncovering new correlations and interdependencies among variables that can drive operational improvements. - Lead the development of scalable data pipelines and integrate model performance management tools into existing business infrastructure. Data Collection, Exploration, and Integration - Access and integrate data from various sources, including SQL databases and other systems relevant to Digital Operations. - Conduct data cleaning, transformation, and preprocessing to ensure data quality and readiness for modeling.





- Apply statistical analysis, such as hierarchical clustering and PCA, to uncover insights. - Aid in the creation of data pipelines for more efficient and repeatable data science projects - Apply statistical analysis and visualization techniques to various data, such as hierarchical clustering and principal components analysis (PCA) - Develop classification and regression models or apply unsupervised learning to aid in uncovering insights in data. Operationalization - Develop advanced analytics models (predictive, prescriptive, machine learning, simulation, statistical) to address complex business challenges in Supply Chain, Manufacturing, and Quality. - Utilize advanced machine learning and statistical techniques to perform data discovery, uncovering new correlations and interdependencies among variables that can drive operational improvements. - Lead the development of scalable data pipelines and integrate model performance management tools into existing business infrastructure. - Collaborate with IT and data engineering teams to deploy and monitor production ML models, establishing best practices around ML production infrastructure. - Continuously monitor, revisit, and refine deployed models to ensure they remain aligned with business objectives and adapt to changing operational needs. - Work closely with stakeholders to communicate findings, insights, and recommendations effectively, ensuring that data-driven strategies are integrated into decision-making processes. Other - Stay updated with the latest advancements in data science, machine learning, and AI, and proactively integrate these innovations into projects where applicable. - Drive strategic initiatives by identifying and prioritizing AI/ML opportunities that align with the organization’s operational goals, challenging the status quo, and promoting bold, innovative thinking.

📌 Data Scientist (México)
🏢 Ingredion
📍 México