Senior Data Scientist

To apply for the Senior Data Scientist position at Sojern, candidates should possess a Master's or PhD in a quantitative discipline such as Economics, Statistics, Data Science, Applied Mathematics, or Computer Science. Additionally, you should have over two years of experience in data science, ideally within the advertising technology sector, and a strong grasp of statistical concepts and causal inference techniques.

As a Senior Data Scientist at Sojern, you’ll play a crucial role by leading data-driven projects to enhance our marketing platform. This includes design and execution of experiments, application of statistical analyses, and communicating insights to improve decision-making and product strategies within a collaborative team environment.

In the Senior Data Scientist role at Sojern, you’ll utilize various analytical tools and programming languages, primarily SQL for data management and Python for data visualization and statistical modeling. Familiarity with Excel and experience with data visualization software like Tableau are also beneficial.

Sojern fosters a supportive and inclusive culture where collaboration and innovation thrive. As a Senior Data Scientist, you’ll engage in cross-functional teamwork, participate in team-building events, and contribute to an atmosphere that emphasizes both personal growth and professional development.

At Sojern, your growth is paramount. The company offers mentorship programs, learning and development stipends, and career development opportunities specifically designed to enhance the skills and knowledge of Senior Data Scientists, ensuring you stay at the forefront of industry advancements.

As a Senior Data Scientist at Sojern, you will have a tangible impact on thousands of clients by developing and implementing data-driven strategies that enhance customer engagement and drive measurable results. Your work in experimentation and analytics directly influences marketing outcomes and client success.

Senior Data Scientists at Sojern lead diverse projects that involve designing and analyzing experiments to improve product performance. This includes applying rigorous statistical methodologies to draw actionable conclusions and presenting these insights to help shape strategic decisions across the organization.

When answering this question, highlight specific statistical methods you're experienced with, such as hypothesis testing, regression analysis, or causal inference techniques. Provide examples of past projects where these methods were crucial in driving insights or decisions.

Discuss the project's goal, the methodologies you used, the challenges faced, and the impact your findings had on stakeholders. Keep your explanation focused on the process and results, emphasizing your problem-solving skills.

To demonstrate your knowledge of maintaining data quality, explain your process for data validation and cleaning. Mention the tools and techniques you use to identify and resolve discrepancies in datasets and the importance of high-quality data in decision-making.

Emphasize your ability to translate complex concepts into relatable insights by using storytelling techniques, visual aids, and clear language. Provide an example of how you successfully communicated findings in the past, ensuring your audience understood the implications.

Outline your familiarity with SQL for querying databases and Python for data analysis, detailing specific libraries you often use like pandas and NumPy. You can discuss a sample project where these skills played a critical role in achieving objectives.

Articulate your belief in the importance of experimentation for hypothesis testing and decision-making. Highlight how structured experiments can lead to actionable insights and discuss frameworks you utilize in your experimentation processes.

Explain your approach to mentorship by sharing techniques you find effective, such as providing constructive feedback, sharing resources, or guiding them in project execution. Discuss any past mentorship experiences and their outcomes.

Discuss factors such as business impact, feasibility, and resource availability that guide your prioritization. Provide an example of a time when you successfully managed multiple projects effectively.

Mention your commitment to continuous learning through various means, including following industry leaders, attending webinars, and participating in online courses. Highlight any recent learnings or trends you have applied successfully in your work.

Share a specific example that outlines the business issue you addressed, the analysis you conducted, and how your findings influenced key decisions. Focus on the value your insights brought to the organization.