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. Requirements 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, TensorFlow, 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. #J-18808-Ljbffr Jobtailor