Data Scientist

As a Data Scientist, you will collaborate with a global, high‑performing team to design, build, and operationalize advanced data science and machine learning solutions. Your work will support customer experience, demand generation/forecasting, operations optimization, and revenue growth initiatives across B2B, and Digital Commerce environments.

This role sits at the intersection of advanced analytics, applied machine learning, and business strategy. You will translate complex data and modeling outcomes into actionable insights while ensuring that models move efficiently from experimentation into scalable, production‑ready solutions.

• Design, develop, and deploy predictive and prescriptive models to support customer experience optimization, demand forecasting, operations efficiency, and customer behavior analysis.
• Apply a range of machine learning and AI techniques, including classical algorithms (e.g., Gradient Boosting, Random Forests, SVMs), deep learning architectures (e.g., LSTM, Transformers), and emerging AI approaches to solve complex business problems.
• Build, maintain, and optimize customer-facing and internal analytical models, such as recommendation systems, churn prediction, propensity modeling, customer segmentation, demand forecasting, matching, and assortment optimization.
• Perform feature engineering, clustering, statistical modeling, and time-series analysis using structured and unstructured data sources.
• Transition models from exploratory research environments (e.g., notebooks) into production-ready artifacts, including APIs, serialized models, and containerized solutions.
• Collaborate closely with engineering teams to ensure end-to-end MLOps integration, including CI/CD pipelines, model versioning, automated deployment, monitoring, and retraining strategies.
• Conduct advanced statistical analysis to identify trends, anomalies, and opportunities, delivering actionable insights through reports and analytical outputs.
• Support experimentation initiatives through A/B testing, KPI definition, causal inference, and measurement of business impact.
• Partner with product, engineering, analytics, and business stakeholders to identify and prioritize data science opportunities aligned with organizational goals.
• Communicate findings clearly, including assumptions, limitations, trade-offs, and recommendations, to both technical and non-technical audiences.
• Contribute to a strong data-driven culture by sharing knowledge, promoting best practices, mentoring peers, and advocating for responsible and ethical use of AI.

• Benefits that go beyond Mexican labor law, ensuring your well-being and peace of mind.
• A collaborative and inclusive work environment where your contributions are valued.
• Opportunities for continuous professional growth and skill development through training, mentoring, and challenging projects.
• Access to cutting-edge tools, resources, and a supportive team to help you excel.
• The chance to work with a global, innovative company shaping the future in its industry. 
   

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