Describe a time you led a complex program involving multiple engineering teams (e.g., AI/ML, platform, and product). How did you ensure alignment and delivery?
I led a cross-org program to integrate an AI-powered recommendation engine into our platform. I established a shared OKR framework, held weekly syncs with leads from each team, and used a RACI matrix to clarify responsibilities. When dependencies surfaced, I facilitated trade-off discussions and escalated to execs only when necessary. The program shipped on time with a 15% increase in user engagement.
How do you approach defining and tracking KPIs for a program that spans infrastructure and product teams?
I start by aligning with each team on the desired business outcome, then decompose it into leading and lagging indicators. For infrastructure, I track latency, uptime, and cost efficiency; for product, I track feature adoption and user satisfaction. I use dashboards to visualize progress and hold monthly reviews to adjust targets based on data.
Can you give an example of how you influenced decision-making without direct authority?
In a prior role, I needed to convince engineering leads to prioritize a platform migration over feature work. I built a data-driven business case showing the long-term cost savings and risk reduction, presented it in a one-pager, and socialized it with key stakeholders. Through one-on-one conversations, I addressed concerns and built consensus, ultimately gaining buy-in from all teams.
How do you handle ambiguity in a new technology area, such as integrating a foundation model?
I start by conducting a spike to explore feasibility, involving subject matter experts to define scope and risks. I break the work into iterative phases, each with clear deliverables and success criteria. I communicate progress frequently to stakeholders, adapting the plan as we learn. This approach reduces uncertainty and builds confidence.
Describe how you have used AI tools to scale your own throughput as a TPM.
I use Claude to draft meeting agendas and summaries, and ChatGPT to generate initial versions of strategy documents. I also leverage AI for data analysis, like parsing logs to identify patterns. This frees up time for higher-level thinking and stakeholder engagement.
