Data Science, Product

For the Data Scientist position at Glean, you'll need a strong foundation in statistics, proficiency in SQL and Python, and experience with BI visualization tools. A background in Statistics, Mathematics, or Computer Science is required, along with at least 4 years in a data science role. Your ability to define KPIs and interpret data will also be vital.

Glean fosters a collaborative and inclusive work culture where creativity and curiosity are encouraged. As a Data Scientist, you'll work closely with diverse teams, including engineering and product management, making every day an exciting opportunity to contribute to innovative AI solutions.

As a Data Scientist at Glean, you will engage in projects that include defining KPIs for AI-powered products, analyzing user engagement data, and developing data pipelines. You'll also explore how Glean's products can enhance knowledge work across different modalities, contributing significantly to product development and strategy.

At Glean, Data Scientists have abundant opportunities for professional growth. You'll be part of a dynamic team that values continuous learning, with access to annual education stipends and mentorship from industry veterans. Moreover, your work directly impacts the company's success, making career advancement attainable.

The salary range for a Data Scientist at Glean is between $175,000 to $250,000 annually based on factors such as your experience and expertise. Additional compensation may include equity and benefits, ensuring we attract top talent.

Glean supports a healthy work-life balance with a flexible work environment and generous time-off policies. Additionally, daily meals and a home office stipend help create a comfortable and productive workspace, allowing you to focus on innovation without stress.

To apply for the Data Scientist position at Glean, you will typically submit your resume and cover letter, highlighting your relevant experience. Our hiring team will review applications and may reach out for an interview where you'll discuss your background and how it relates to Glean's mission and goals.

When answering this, focus on the problem you aimed to solve, the specific data science techniques you used, and the positive outcomes of your project. Providing quantifiable results will strengthen your case and demonstrate your impact on the business.

Define KPIs by considering the product's purpose and objectives. Discuss how you involve stakeholders in determining what metrics matter, emphasize the importance of aligning KPIs with business goals, and mention how you regularly review and adjust these metrics.

Discuss a few statistical models you’re proficient in, providing context on how you've applied them in your work. Mention specific scenarios where these models helped achieve insights or informed business decisions, showcasing practical application.

Describe your approach to data analysis, including data collection, data cleansing, and specific analysis techniques you’d employ, such as A/B testing or cohort analysis. Highlight tools you use and how insights shaped product development or marketing strategies.

Discuss your time management strategies, such as prioritizing tasks based on their business impact and using project management tools. It’s also important to emphasize the importance of communication with your team to align on goals and deadlines.

Use the STAR method (Situation, Task, Action, Results) to frame your answer, detailing how your insights changed the product roadmap or strategy. Be clear on the business outcome and your role in facilitating this change.

Talk about the specific BI tools you have experience with, such as Tableau or Looker, citing examples of dashboards you built or how you used visualization to convey complex data insights to stakeholders, enhancing decision-making processes.

Outline your systematic approach to experimentation, considering hypothesis formation, experimental design, and adherence to best practices in A/B testing. Discuss past experiments to illustrate your points and the learning derived from them.

Discuss your proficiency level with SQL, providing examples of complex queries or data extraction tasks you have successfully completed. Highlight how your SQL skills have enabled you to derive insights from large datasets and contribute to data-driven decisions in your previous roles.

Share your methods for creating clear and concise technical documentation, discussing the importance of structured content, audience understanding, and ensuring that your documentation can be easily referenced by peers or new team members.