Director, Data Science - job 1 of 2

For the Director of Data Science at Known, key skills include advanced quantitative analysis, strong proficiency in Python and SQL, and comprehensive experience in building data science software. Additionally, superb communication and presentation skills are essential for effectively conveying complex data insights to clients and teams.

In the role of Director, Data Science at Known, you will be responsible for developing and implementing advanced machine learning models, optimizing data processes, mentoring fellow data scientists, and interfacing with cross-functional teams. Your focus will be on leveraging data to create valuable tools that enhance our media buying practices.

Candidates for the Director, Data Science position at Known should ideally have a master’s or PhD in a STEM field, along with over 7 years of hands-on experience in quantitative analysis and at least 5 years in building data science solutions. Experience in advertising technology, while not mandatory, is considered a plus.

At Known, the culture for data science professionals is characterized by collaboration, curiosity, and a commitment to continuous improvement. Data scientists work closely with creative teams, sharing insights and learning from each other to drive innovation in marketing through data science.

Yes! As a Director, Data Science at Known, you will not only lead your projects but also play a significant role in mentoring junior data scientists, paving the way for both your professional growth and the development of your team. Known values continuous learning and improvement among its employees.

In answering this, focus on your methodology for data collection, selecting relevant features, creating models, and evaluating their performance. Discuss your experience with specific tools and algorithms, as well as any lessons learned from past projects.

When responding, select a project that showcases your leadership and technical skills. Clearly outline your role, the challenges faced, the techniques used, and the impact of the project's outcome on your organization or clients.

Emphasize your practices regarding data validation, model testing, and ongoing performance monitoring. Discuss any standard processes your team follows to maintain quality and details about how you handle discrepancies.

List relevant tools like Python, SQL, or specific machine learning libraries you’ve used. Highlight your proficiency in cloud computing and data processing technologies and relate them back to your project experiences.

Share strategies such as attending workshops, online courses, conferences, or participating in data science communities. Highlight specific advancements that interested you and how you have or will implement them in your work.

Discuss your approach to mentorship, emphasizing the importance of communication, patience, and providing constructive feedback. Share an example of a successful mentorship experience to illustrate your points.

Explain your process for understanding client objectives and how you break down those needs into measurable data science tasks. Use a past experience to demonstrate your effective communication with stakeholders.

Choose a specific example demonstrating your analytical thinking and problem-solving skills. Describe the context, your thought process, and the solution implemented, ensuring you convey its significance to the business.

Detail your experience with workflows involving data collection, cleaning, and preprocessing. Mention any tools you've used for building data pipelines and discuss the importance of efficiency in managing data flows.

Share insights into your collaborative and supportive approach. Talk about how you cultivate an environment of innovation and encourage creativity while maintaining focus on project goals and team development.