Machine Learning Scientist III – Whole Trip AI

• Design and implement end-to-end model pipelines to production across multiple product domains, including ranking, recommendations, search, and personalization
• Develop and maintain scalable data pipelines, data quality checks, and model monitoring to ensure reliability, performance, and responsible behavior of ML systems in production
• Collaborate with cross-functional partners (product, analytics, engineering) to translate ambiguous business needs into well-scoped ML projects, communicate findings, and influence decision making with data-driven insights
• Use A/B tests and offline/online evaluation frameworks to measure model impact and guide iterative improvement

• Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience
• 2+ years of relevant professional experience
• Professional industry experience applying machine learning or statistical modeling to real business problems, including end-to-end model development from data exploration through evaluation and deployment
• Proficiency in Python and with ML frameworks and libraries for model development, training, and evaluation
• Demonstrated ability to translate problem statements into well-defined ML tasks, design appropriate model and data structures (including APIs and data models), and own solutions within a defined product, service, or feature area, including familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products with attention to safety and reliability.

Expertise in designing and implementing end-to-end machine learning model pipelines, with a strong focus on data quality, model monitoring, and collaboration with cross-functional teams to drive data-driven decision making.

• Machine Learning Model Development
• Python Proficiency
• Data Pipeline Development
• A/B Testing
• Statistical Modeling

• Machine Learning
• Statistical Modeling
• Model Evaluation
• Data Exploration
• Model Deployment
• Data Quality Checks
• Model Monitoring
• API Design
• Data Structures
• AI/ML Concepts

• Collaboration
• Communication
• Influencing Decision Making

• End-to-End Model Development
• Cross-Functional Collaboration
• Product Domains
• Personalization
• Recommendations
• Search

• ML Frameworks
• Data Pipelines
• Evaluation Frameworks