Sr. Manager, Yield Management - GTM Advanced Analytics

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In this position...

We are seeking a visionary and experienced Senior Manager to lead the strategic development, deployment, and continuous enhancement of cutting-edge AI/ML solutions for Yield Management. In this pivotal role, you will lead a high-performing team to transform business processes through predictive demand forecasting, dynamic segmentation, sensitivity analysis, and optimization algorithms. The ideal candidate blends deep technical expertise in AI/ML engineering with a proven track record of delivering end-to-end production-grade AI solutions that drive measurable business impact.

We are seeking a Senior Manager to lead a high-performing team in developing and deploying advanced AI/ML solutions for Yield Management. In this role, you will drive business transformation through predictive forecasting, dynamic segmentation, and optimization. The ideal candidate combines deep technical AI/ML engineering expertise with a proven track record of delivering production-grade solutions that drive measurable business impact.

What you'll do...

Strategic AI/ML Leadership & Delivery

End-to-End Lifecycle Management: Lead the design, validation, deployment, and monitoring of production-grade AI/ML solutions (e.g., predictive modeling, forecasting, optimization, and segmentation).

Demand Sensing & S&OP: Lead the development, maintenance, and optimization of advanced demand sensing algorithms to support sales and operations planning (S&OP) and yield management.

Roadmap & Innovation: Drive the strategic AI/ML roadmap, identifying innovative use cases-including Agentic AI for workflow automation-to deliver significant business value.

Rapid Prototyping: Foster a culture of rapid prototyping and rigorous testing to quickly validate the business utility of new AI/ML applications.

Technology Strategy & MLOps Excellence

Tech Stack Evolution: Evaluate and adopt emerging AI/ML platforms, frameworks, and methodologies to enhance solution efficiency, scalability, and performance.

MLOps & Governance: Establish and enforce robust MLOps practices, model governance, version control, and comprehensive documentation to ensure seamless deployment and retraining.

Responsible AI: Champion ethical AI principles, ensuring fairness, transparency, and data privacy are embedded across the model lifecycle.

Data Quality: Oversee data management practices (sourcing, cleaning, preprocessing) to ensure high-quality data pipelines for all AI/ML models.

Cross-Functional Collaboration & Stakeholder Engagement

Strategic Alignment: Act as the primary liaison between technical teams and business stakeholders (Sales & Marketing, Finance, IT) to align AI/ML initiatives with corporate strategy.

Executive Communication: Translate complex algorithmic concepts and technical outcomes into clear, actionable insights and recommendations for executive leadership.

People Leadership: Guide and mentor data scientists and AI/ML engineers in technical execution, professional growth, and stakeholder management.

What you'll do...

Strategic AI/ML Leadership & Delivery

End-to-End Lifecycle Management: Lead the design, validation, deployment, and monitoring of production-grade AI/ML solutions (e.g., predictive modeling, forecasting, optimization, and segmentation).

Demand Sensing & S&OP: Lead the development, maintenance, and optimization of advanced demand sensing algorithms to support sales and operations planning (S&OP) and yield management.

Roadmap & Innovation: Drive the strategic AI/ML roadmap, identifying innovative use cases-including Agentic AI for workflow automation-to deliver significant business value.

Rapid Prototyping: Foster a culture of rapid prototyping and rigorous testing to quickly validate the business utility of new AI/ML applications.

Technology Strategy & MLOps Excellence

Tech Stack Evolution: Evaluate and adopt emerging AI/ML platforms, frameworks, and methodologies to enhance solution efficiency, scalability, and performance.

MLOps & Governance: Establish and enforce robust MLOps practices, model governance, version control, and comprehensive documentation to ensure seamless deployment and retraining.

Responsible AI: Champion ethical AI principles, ensuring fairness, transparency, and data privacy are embedded across the model lifecycle.

Data Quality: Oversee data management practices (sourcing, cleaning, preprocessing) to ensure high-quality data pipelines for all AI/ML models.

Cross-Functional Collaboration & Stakeholder Engagement

Strategic Alignment: Act as the primary liaison between technical teams and business stakeholders (Sales & Marketing, Finance, IT) to align AI/ML initiatives with corporate strategy.

Executive Communication: Translate complex algorithmic concepts and technical outcomes into clear, actionable insights and recommendations for executive leadership.

People Leadership: Guide and mentor data scientists and AI/ML engineers in technical execution, professional growth, and stakeholder management.