Director, Machine Learning

The Director of Machine Learning at Viant is responsible for providing strategic and technical leadership to the Machine Learning Engineering team, which includes overseeing the end-to-end development and maintenance of machine learning models. You will also cultivate a high-performance culture, utilize advanced techniques to address complex business challenges, and collaborate with cross-functional teams to drive innovation and insights.

To qualify for the Director of Machine Learning position at Viant, candidates should have over 10 years of experience in machine learning, including 5+ years in leadership roles. A Bachelor's degree in Computer Science, Engineering, or a related field is required, while a Master's degree or PhD in machine learning is preferred. Strong expertise in real-world applications of AI and a track record of delivering results at scale are crucial.

Success as a Director of Machine Learning at Viant hinges on a blend of technical and leadership skills. You should have exceptional problem-solving abilities, an aptitude for fostering collaboration across diverse teams, and a strong understanding of advanced machine learning methods. Experience in the Ad Tech industry will also be highly beneficial.

The machine learning models developed by Viant play a critical role in automating the processes involved in creating, launching, and measuring digital ads. They help in content demand forecasting, personalization, and measuring incremental value, significantly enhancing the precision and efficiency of advertising campaigns.

At Viant, the Director of Machine Learning has access to growth and development opportunities through leading innovative projects, collaborating with cross-functional teams, and nurturing a high-performance engineering culture. Additionally, the emphasis on mentoring and knowledge sharing among team members fosters an environment rich in learning and professional development.

In answering this question, provide specific examples of machine learning models you've developed and their impact on business outcomes. Discuss your approach to model design and deployment, emphasizing metrics that demonstrate success.

Demonstrate your ability to prioritize by discussing your approach to evaluating project deadlines, resource availability, and overall business impact. Explain any frameworks or tools you use to keep projects aligned with goals.

Highlight your familiarity with techniques such as supervised learning, unsupervised learning, and causal inference. Explain how you've applied these in past roles, particularly in the context of enhancing advertising strategies.

Discuss key performance indicators (KPIs) that are relevant in measuring machine learning success, such as accuracy rates, business ROI, or operational efficiency improvements. Provide examples of how you've tracked and reported on these metrics.

Talk about your leadership style and strategies, such as implementing coaching sessions, feedback mechanisms, and creating an environment encouraging innovation and collaboration. Discuss past experiences where these strategies led to tangible improvements.

Share a specific case where you identified a significant challenge and utilized machine learning techniques to address it. Describe the problem-solving process you employed, the outcome, and what you learned from the experience.

Emphasize the importance of continuous learning in your field. Mention any conferences, workshops, or online courses you have recently taken, as well as noteworthy publications or authors that influence your approach.

Provide examples of how you've effectively collaborated with product, finance, and other teams in previous roles. Highlight specific projects where interdepartmental teamwork led to successful outcomes.

Discuss your strategies for ensuring that machine learning models can be effectively scaled, such as focusing on modularity, performance testing, and optimizing for speed and resource utilization.

Articulate your understanding of the ethical implications of machine learning, sharing your perspective on fairness, transparency, and accountability in model development. Provide examples of how you've integrated ethical considerations in past projects.