One can build critical thinking on the job by analyzing data, balancing customer and business needs, and seeking feedback on your work. You should also complement this with external learning through product communities, case studies, and product critiques to expose yourself to diverse perspectives. On-the-Job Learning Analyze data critically: You should regularly review product and business data. Ask yourself questions like "What does this data actually tell me?" and "What could explain these tr ...Read More
Ruchi Aggarwal
Director, Product Management - Payments at Former BILL
Content
Yes, Product Ops is critical, especially in FinTech where money movement is involved. I like to involve them end-to-end: pre-launch for requirements and operational risks, during development to validate scope changes, and before release through demos and readiness checks. Post-launch, they validate production behavior, surface issues, and help prioritize fixes. It’s a tight partnership that ensures smooth launches and a strong customer experience.
When hiring a PM or Senior PM, I focus on product sense, execution skills, and a hunger to learn. Key Evaluation Criteria: Soft Skills: Do they clearly understand the problem they were solving and why it mattered? Can they articulate key decisions, their rationale, and how they approached them? How do they reflect on challenges and share how they navigated them? Do they evaluate the impact of their work and demonstrate self-awareness about what could have been done differently? Hard Skills: Can ...Read More
I triage scope changes based on impact: does the new info affect MVP success, expose a missed requirement, or can it wait post-launch? If it’s critical, I evaluate timeline impact and what we can de-scope or shrink to absorb it. If not, it goes to backlog. Once decided, I communicate early and clearly on what changed, why, and the trade-offs. This keeps iteration fast without losing control
As part of my self-evaluation, I focus on understanding what the next level demands and identifying the skills or behaviors I need to learn and demonstrate to get there. I actively seek feedback from my manager and trusted peers to gain clarity on expectations and uncover blind spots. Regular self-reflection helps me track progress, align my work with company goals, and recognize opportunities to stretch myself. To support colleagues, I encourage open conversations about career goals, provide co ...Read More
Skills Across Product Management Levels Early PM:Focus on mastering execution. You'll likely be given problems and potential solutions, so use this time to understand the why behind the work—why it helps customers or the business. Build foundational skills in working with cross-functional teams, especially engineering and support. Treat these as opportunities to learn and refine your craft. Mid-Level PM (Senior / Experienced PM):At this stage, you should own problem-solving. You’ll be handed cus ...Read More
The biggest misconception is believing AI will do everything for you- define the MVP, decide what to build, and evaluate itself. It won’t. You still need clear requirements, scope, and a definition of “good.” AI is powerful, but only when paired with strong product thinking. You have to guide it, structure the problem, and feed it the right context. Otherwise, it produces noise, not outcomes.
I stay organized by setting half-yearly goals aligned with my manager. I like to break them down into monthly goals prioritized by impact, and track weekly progress, plans, and problems. This ensures alignment and helps me spot early signs of deviation from the original plan. You can use a simple spreadsheet or leverage tools your company provides, but the core idea is to maintain visibility into progress and continuously tie short-term actions to long-term objectives. This practice keeps me foc ...Read More
For big launches, I run weekly “office hours” with support, Product Ops, engineering, and any ops partners (in FinTech, often legal/compliance). We review early metrics together, separate signal from noise, and pair that with qualitative feedback from the field. Actions may go to engineering, product, or ops (SOP updates). We keep this running for weeks or months until GA, and the insights feed directly into the roadmap and how we run the next cycle
I use a simple filter: if the problem is deterministic, rules-based, and predictable, traditional software is better. AI shines when there’s lots of data, high variability, and many “it depends” scenarios where different users need different answers. I also check if we can reliably measure correctness i.e. AI must be evaluable. If the task benefits from pattern-matching, reasoning, or personalization at scale, it’s a strong candidate for AI.