Director AI ML

Summary: The Director of AI ML will be a key strategic and technical leader within ADT’s Data & AI organization. This individual will be responsible for leading a high-performing team of data scientists and machine learning engineers to tackle our most challenging business and technical problems. Leveraging deep expertise in statistical modeling, machine learning, and scalable AI, the Director will translate ADT's vast data assets—from smart home IoT telemetry to customer touchpoints—into actionable business value and predictive products. The ideal candidate is an inspiring people leader, a hands-on strategic thinker, and a creative problem-solver. This role requires a strong balance of technical depth, people management, and the business acumen necessary to collaborate closely with executive stakeholders while delivering within real-world constraints Duties and Responsibilities: Team Leadership & People Management: Build, lead, and inspire a world-class team of data scientists; provide active mentorship, career development, and technical guidance. Strategic AI/ML Roadmap: Define and execute the data science roadmap, aligning technical initiatives with ADT's broader business goals, customer retention strategies, and product development. End-to-End Delivery: Oversee complex data science projects from conception through to deployment, ensuring models are scalable, robust, and successfully integrated into production systems. Advanced Technical Stewardship: Ensure the team applies rigorous statistical modeling, machine learning, and data mining techniques to drive measurable business growth. Cross-Functional Collaboration: Partner closely with Marketing, Sales, Operations, and Executive leadership to identify high-impact business opportunities and design AI-driven solutions. Data Pipeline & Infrastructure Strategy: Collaborate with data platform and engineering teams to champion modern MLOps practices, streamline data pipelines, and improve experimental velocity. Executive Communication: Translate highly technical and complex data science concepts, model performance metrics, and strategic recommendations to both technical and non-technical audiences. Measurement & Experimentation: Champion experimental design (A/B testing) and advanced measurement strategies to robustly validate the impact of deployed data products.

Master’s degree or PhD (preferred) in a quantitative field such as Computer Science, Statistics, Mathematics, or a related discipline.