Senior Applied Scientist/Data Scientist

As a Senior Applied Scientist at Sojern, your primary responsibilities include designing machine learning models for optimizing advertising campaign performance, analyzing key metrics such as conversion rates, and implementing solutions based on business requirements. You'll write production-level Python code, manage complex SQL queries, and collaborate actively with other team members throughout the Agile development lifecycle.

To succeed as a Senior Applied Scientist at Sojern, you need at least two years of experience in a similar role, an MS or PhD in computer science or a quantitative discipline, and strong expertise in machine learning. Familiarity with cloud computing platforms like Google Cloud is essential, as is experience in writing production-level code and performing complex data analysis.

At Sojern, Senior Applied Scientists work with a variety of technologies including TensorFlow, Google Cloud Platform, and Docker. You’ll have hands-on experience with machine learning pipelines and data management, utilizing tools that help enhance the effectiveness of our marketing campaigns.

Sojern fosters a vibrant learning culture by offering mentorship programs, learning stipends, and career development resources. As a Senior Applied Scientist, you’ll have ample opportunities to grow professionally while working on innovative projects and collaborating with experienced peers.

Absolutely! Sojern is committed to building a diverse and inclusive workforce. We have several employee resource groups aimed at supporting various communities and ensuring all voices are heard. Our culture emphasizes appreciation of diverse perspectives, which enhances our work environment and solutions.

When answering this question, discuss specific machine learning models you've built, the problems they solved, and their impact on campaign performance. Highlight your experience with metrics like conversion rates and click-through rates, and how these influenced your model design.

Focus on talking about coding best practices such as writing clean, modular code, using appropriate data structures, and implementing algorithms that prioritize performance. Mention debugging tools and libraries you leverage to assess code efficiency.

Share a specific example that illustrates your analytical skills. Explain the problem, the tools and methodologies you used, and how your solution not only resolved the issue but also added value to the project or the team.

Discuss your exposure to Google Cloud services, detailing any projects you've implemented on the platform. Talk about services like BigQuery, TensorFlow on GCP, and how they contributed to the success of your projects.

Emphasize the importance of communication and collaboration tools. Describe specific techniques like regular stand-ups, version control systems, and knowledge-sharing sessions to keep everyone aligned on project goals.

Discuss algorithms you’ve worked with, such as supervised learning, unsupervised learning, and reinforcement learning. Provide examples of projects where you applied these algorithms and the results you achieved.

Explain your methodology for designing A/B tests, including how you establish control and variable groups, how you measure success, and how you iterate based on results. Share an example of an A/B test you ran and the insights gained.

Talk about the visualization tools you prefer, such as Tableau, matplotlib, or Google Data Studio. Give examples of how you've utilized them to present data findings and assist in decision-making processes.

Provide a specific scenario where your analytical skills led to actionable insights. Highlight how these insights influenced campaign strategies, resource allocation, or customer engagement efforts.

Talk about the balance of technical expertise and communication skills. Emphasize the ability to translate complex data findings into understandable insights for non-technical stakeholders, which ensures alignment on project objectives.