Senior Manager, Yield Management – GTM Advanced Analytics

Responsibilities

• Lead the design, validation, deployment, and monitoring of production‑grade AI/ML solutions (e.g., predictive modeling, forecasting, optimization, and segmentation).
• Lead the development, maintenance, and optimization of advanced demand sensing algorithms to support sales and operations planning (S&OP) and yield management.
• Drive the strategic AI/ML roadmap, identifying innovative use cases—including Agentic AI for workflow automation—to deliver significant business value.
• Foster a culture of rapid prototyping and rigorous testing to quickly validate the business utility of new AI/ML applications.
• Evaluate and adopt emerging AI/ML platforms, frameworks, and methodologies to enhance solution efficiency, scalability, and performance.
• Establish and enforce robust MLOps practices, model governance, version control, and comprehensive documentation to ensure seamless deployment and retraining.
• Champion ethical AI principles, ensuring fairness, transparency, and data privacy are embedded across the model lifecycle.
• Oversee data management practices (sourcing, cleaning, preprocessing) to ensure high‑quality data pipelines for all AI/ML models.
• Act as the primary liaison between technical teams and business stakeholders (Sales & Marketing, Finance, IT) to align AI/ML initiatives with corporate strategy.
• Translate complex algorithmic concepts and technical outcomes into clear, actionable insights and recommendations for executive leadership.
• Guide and mentor data scientists and AI/ML engineers in technical execution, professional growth, and stakeholder management.

• Master’s in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Statistics, or a closely related quantitative field.
• 10+ years of progressive experience in AI/ML engineering or data science, with 3–5 years of experience directly leading and managing technical teams.
• Proven experience building and deploying production‑grade predictive models, time‑series forecasting, and optimization algorithms.
• Hands‑on experience building and deploying Agentic AI solutions for complex workflow automation.
• Advanced proficiency in Python and core ML libraries (e.g., PyTorch, Tensor Flow, scikit‑learn). Strong command of SQL.
• Proven experience deploying and managing AI/ML workloads on cloud platforms (AWS, Azure, or GCP) using MLOps tools.
• Experience developing user‑facing applications or dashboards (e.g., Streamlit, Dash, Flask/Django) for model consumption.