Senior Data Science Engineering Manager, Marketing

At Draft Kings, AI is becoming an integral part of both our present and future, powering how work gets done today, guiding smarter decisions, and sparking bold ideas. It’s transforming how we enhance customer experiences, streamline operations, and unlock new possibilities. Our teams are energized by innovation and readily embrace emerging technology. We’re not waiting for the future to arrive. We’re shaping it, one bold step at a time.

To those who see AI as a driver of progress, come build the future together.

As a Senior Data Science Engineering Manager, Marketing, you’ll set the strategy for how data science and machine learning power personalized experiences for millions of customers. You’ll lead and grow a team of data scientists and machine learning engineers, turning complex opportunities into focused priorities and scalable solutions. In partnership with our Product, Engineering, Marketing, and Revenue teams, you’ll connect technical investments to business priorities and deliver measurable customer impact.

You’ll also help shape the broader direction of our data science capabilities, raising technical standards and advancing how we approach personalization at scale.

• Lead, coach, and develop a high-performing team of data scientists and machine learning engineers, strengthening the technical and leadership capabilities needed to deliver personalization initiatives at scale.

• Set your team’s strategy, roadmap, and priorities, translating business goals and ambiguous opportunities into high-impact data science and machine learning initiatives with clear measures of success.

• Partner with senior leaders across Product, Engineering, Marketing, Revenue, and Data Science to identify opportunities, navigate tradeoffs, and connect technical decisions to customer and business outcomes.

• Guide the development and deployment of machine learning systems across the full model development lifecycle, establishing rigorous standards for design, development, testing, deployment, monitoring, and iteration.

• Influence the architecture of model-driven experiences, including data pipelines, feature generation, experimentation, batch and real‑time serving, observability, and model lifecycle management.

• Advance software engineering and machine learning operations practices across the team, including automation, containerization, testing, monitoring, and reliable production deployment.

• Own capacity planning and resource allocation, balancing business value, technical investment, operational needs, and team capacity while adjusting priorities as opportunities and risks evolve.

• Build a culture of experimentation, technical excellence, and continuous improvement that empowers your team to solve increasingly complex challenges and deliver measurable impact.

• A Bachelor’s Degree in Computer Science, Data Science, Machine Learning, or a related field; an advanced degree is preferred.

• At least 10 years of experience in data science or machine learning engineering, including at least 5 years of formal people leadership or management experience.

• Proven experience leading data science or machine learning teams through complex initiatives, from setting technical direction through production deployment and ongoing optimization.

• Advanced proficiency in Python and SQL, along with experience working with modern data platforms and cloud environments.

• Deep knowledge of machine learning systems, statistical modeling, experimentation frameworks, and the software development lifecycle.

• Experience applying software engineering, Dev Ops, and machine learning operations practices, including containerization, automation, testing, deployment, and production monitoring.

• Strong communication…