Product Director - Machine Learning

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Customer Success Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. Department Overview The Analytics Tools Team within Salesforce’s Customer Success Group (CSG) helps customers unlock the full value of their Salesforce investments through data-driven insights. We build and manage three critical products that provide a comprehensive view of the customer experience: Customer Success Score, which delivers actionable recommendations to measure and improve success; Proactive Monitoring, which offers 24/7 automated alerts to detect and resolve issues early; and Attrition Risk Insights, an ML-powered solution that identifies churn risk and its root causes so teams can take timely, targeted action. Role Overview As the Machine Learning Product Director for Attrition Risk Insights , you own the end-to-end Machine Learning strategy behind the company’s source of truth for customer risk. Attrition Risk Insights uses Machine Learning at its core to identify churn risk and its root causes so teams can take timely, targeted action. In close partnership with the Data Science team, you will co-own the modeling roadmap, experimentation strategy, and model performance—ensuring accuracy, explainability, and measurable business impact. You will serve as the bridge between the Data Science team and product consumers, translating business use cases and customer needs into clear technical product requirements. Together, you will empower Customer Success, Renewals, and Sales to proactively mitigate churn risk with Attrition Risk Insights. Key Responsibilities Own ML Strategy & Performance: Define the ML roadmap, success metrics (technical and business), experimentation plans, and model iteration lifecycle from deployment through monitoring and retraining. Operationalize ML Insights: Embed risk predictions, confidence signals, and explainability into field workflows to drive clear, actionable mitigation steps. Drive Trust & Continuous Improvement: Establish feedback loops, monitor drift and bias, and continuously improve model accuracy, calibration, and adoption. Lead Cross-Functional Alignment: Serve as the bridge between Data Science, Engineering, and Field teams to ensure scalable, responsible, and high-impact ML execution. Minimum Requirements Strategic, Analytical, and Collaborative Skills: Strong strategic thinker with an ability to leverage advanced data analysis and predictive modeling to drive customer engagement and product improvement. Skilled at collaborating and influencing across all levels of an organization, especially in driving alignment with executive and senior leadership. Product Management Leadership: Minimum of 10 years of product management experience with demonstrated leadership in highly matrixed, cross-functional environments. Must have a proven track record of successfully scaling products to support hundreds of thousands of customers. Machine Learning-focused Product Management: 5 years of experience collaborating with Data Science or Machine Learning model development teams. Educational Background: Bachelor’s degree in Business, Engineering, Marketing, Data Science, or a related field; MBA or equivalent experience preferred. Why Join the Salesforce Customer Succ ... (truncated, view full listing at source)