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.
YOU MUST HAVE
3+ years of hands-on experience in applied data science and machine learning, ideally within B2B, Retail, or Digital Commerce environments.
Strong proficiency in Python, including experience with data science and ML libraries such as Pandas, NumPy, Scikit‑learn, and visualization tools.
Advanced SQL skills, including the ability to write complex queries, procedures, and analytical transformations.
Solid understanding of classical machine learning algorithms and their real-world business applications.
Strong analytical, critical-thinking, and problem-solving abilities with a consistent focus on business impact.
Professional working proficiency in English for global collaboration and technical communication.
WE VALUE
Experience applying deep learning techniques, including sequence models (LSTM) and Transformer-based architectures.
Hands-on experience leveraging Large Language Models (LLMs) for use cases such as text classification, search, summarization, enrichment, or automation.
Practical experience designing or improving recommendation systems and personalization models.
Familiarity with MLOps best practices, including CI/CD pipelines, automated testing, deployment, monitoring, and retraining.
Experience with Snowflake or similar cloud-based data warehouse platforms.
Exposure to data engineering concepts, such as data pipelines, dbt, workflow orchestration, or streaming architectures.
Experience working with Azure cloud services and ML/AI platforms.
Proficiency with GitHub for version control and collaborative development workflows.
Experience working in Agile/Scrum environments.
Strong collaboration skills, intellectual curiosity, adaptability, and a commitment to continuous learning.
WHAT'S IN FOR YOU
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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