Data Scientist
HybridQuerétaro, Chiapas, Mexico
Job Summary
Design, develop, and deploy predictive and prescriptive models for customer experience, demand forecasting, and operations efficiency using machine learning and AI techniques. Build and maintain analytical models such as recommendation systems, churn prediction, and propensity modeling while performing feature engineering and time-series analysis. Transition models from exploratory research into production-ready artifacts with full MLOps integration, including CI/CD pipelines, automated deployment, and monitoring. Collaborate with engineering and business stakeholders to prioritize opportunities, conduct A/B testing, and communicate findings to technical and non-technical audiences. This role sits at the intersection of advanced analytics and business strategy, supporting revenue growth initiatives across B2B and Digital Commerce environments within a global, high-performing team.
Required Qualifications
- 3+ years of hands-on experience in applied data science and machine learning, ideally within B2B, Retail, or Digital Commerce environments
- Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Analytics, or a related field
- 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
Desired Qualifications
- 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
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