Senior Manager, Applied Science

Overview In this role you will lead Viant’s ML Engineering team and shape the strategic direction of applied science initiatives. You will own the end-to-end lifecycle of ML models powering Viant’s products, from development to deployment and maintenance. You’ll coach and grow a high-performance team, fostering engineering excellence and scalable practices. You will tackle real-world problems like demand forecasting, value measurement, and personalization, driving measurable business impact. This position offers the chance to influence product strategy and advance autonomous advertising solutions at scale. Compensation / Benefitsfully paid health insurancepaid parental leaveunlimited PTO Responsibilities Provide strategic and technical leadership to the ML Engineering team to drive innovation and align with company objectives Own end-to-end development, deployment, and maintenance of ML models powering Viant’s products Develop and coach a team of ML engineers, fostering engineering excellence and quality Apply advanced ML techniques including supervised learning and causal inference to solve business problems Collaborate with Product, Finance, and Content leadership to deliver insights and guide decisions Drive research and adoption of new methodologies and ML approaches Build and nurture stakeholder relationships to ensure timely delivery of impactful ML solutions Key requirements 7+ years of experience in machine learning 3+ years of experience leading and scaling high-performing teams Bachelor’s degree in Computer Science, Engineering, or related field; Master’s preferred Strong expertise in developing and applying ML models for real-world applications Proven ability to deliver results at scale through innovation and operational excellence Exceptional problem-solving skills and ability to tackle novel challenges Demonstrated track record of cross-functional collaborationleadership and people managementcross-functional collaborationstrategic thinkingmachine learning models for real-world applicationssupervised learningcausal inference