Data Science Tech Lead Manager, Core Product

As a Data Science Tech Lead Manager at Glean, your primary responsibilities include guiding and managing a team of data scientists, defining data assets, creating data pipelines, and developing dashboards to assess the performance of AI-powered products. You will also identify opportunities for improving key performance indicators and work with cross-functional teams to integrate changes into their product roadmaps.

To qualify for the Data Science Tech Lead Manager role at Glean, candidates should have a Bachelor's, Master's, or PhD in Statistics, Mathematics, Computer Science, or a related field, along with a minimum of 8 years of experience as a data scientist. Additionally, at least 3 years of hands-on management experience with teams of 5 or more is required. Proficiency in SQL, Python, and BI visualization tools is essential.

Glean's Data Science organization comprises data science, applied science, data engineering, and business intelligence groups. As a Data Science Tech Lead Manager, you will play a pivotal role in ensuring cohesion between different data teams, while also acting as a point of contact for high-profile product initiatives and collaborating closely with engineering and product management.

Glean is committed to attracting and retaining a diverse workforce, promoting an inclusive culture that respects individuality. As a Data Science Tech Lead Manager, you will be part of initiatives aimed at fostering diversity in the team and workplace, ensuring that every member's unique background and perspectives contribute to our collective success.

Glean provides a competitive compensation package, which includes medical, vision, and dental coverage, a flexible work environment, a 401k plan, company events, and stipends for home office improvements and education. Employees also enjoy wellness stipends and daily healthy meals, reinforcing Glean's commitment to employee well-being.

Defining key performance indicators involves aligning with stakeholders to identify goals that reflect the success of our AI products. It's essential to ensure that these KPIs are measurable and actionable, facilitating data-driven decisions that enhance product performance.

Highlighting experience managing a team involves discussing your approach to leadership, such as fostering collaboration and encouraging professional growth. Emphasize your hands-on involvement and strategies for maintaining team cohesion while achieving project goals.

I utilize data analytics and feedback from user engagement to pinpoint areas where KPIs can be enhanced. This often includes A/B testing to experiment with changes and assess their impact on user behavior and product performance.

Effective collaboration with cross-functional teams involves regular communication, understanding each department's objectives, and aligning data science efforts with product management and engineering goals. I prioritize building relationships to facilitate smoother workflows.

Essential tools include SQL for database management, along with BI visualization tools like Tableau or Looker for developing intuitive dashboards. Additionally, being proficient in Python for scripting and data manipulation is crucial for effective data pipeline management.

Describe a project that had clear objectives, the methods you used to achieve success, and the impact it had on the product or business. Use quantifiable metrics to showcase improvements resulting from your leadership and data-driven insights.

I encourage my team to approach data with a storytelling perspective, ensuring that we communicate our findings effectively to stakeholders. Terms like strategic insights, visualizations, and clear narratives help ensure our analyses drive actionable outcomes.

Technical documentation is essential for standardizing processes, sharing knowledge with the team, and ensuring that data decisions are transparent. I emphasize clarity and precision in documentation to help my team and future data scientists understand our methodologies.

Prioritizing tasks involves assessing the impact of each project, aligning them with company objectives, and navigating dependencies. I hold regular check-ins to ensure the team is focused, while remaining adaptable to shifts in priorities.

My approach includes leveraging AI tools to automate routine tasks, enabling data teams to focus on high-value analytical work. Additionally, I advocate for training non-data professionals to utilize these tools effectively for better decision-making across the organization.