Product School hosted Nagu Rangan, a Principal Product Manager at Microsoft for a #AskMeAnything session. Nagu answered questions about key skills for a Principal PM, transitioning into Product, and advice on building ML/AI products.
Nagu is a Principal Product Manager for Search & AI at Microsoft. He is extremely passionate about delivering great customer experiences. Nagu has a strong track record of managing products (especially ones driven by machine learning) from inception to launch with a focus on user experience and product strategy.
He has 12+ years of experience delivering large-scale hardware and software products. Prior to his role at Microsoft, Nagu was a Senior Product Manager at Amazon where he Co-led the growth hacking initiative that led to the first time Amazon app became one of the ten most downloaded apps in the Apple App Store.
“What are the key skills you need as a Principal PM vs a Senior PM?”
Simple answer is Scope & Scale. Essentially, complexity of problems (cross org, ambiguous), the expected impact/ROI, the level of autonomy expected & clarity in communication (written & oral).
Check out: The Skills Product Managers Need in 2021
“Do you think it’s the right course of action to be a Product Owner and working my way up to be a Product Manager?”
The path varies from person to person. How I would do is, map my short term, mid term and long term goals. Reach out to folks who are doing the role you would want to do, do a gap analysis and map your journey.
Looking to transition? Here’s your guide to Transitioning to Product Management From ANY Background
“What is the difference between a program manager and product manager in Microsoft?”
Essentially the same. I have heard that the term “program” was used since the products are essentially computer programs (all the way from end customer applications like browser/office applications to layers in the OS).
“What was your experience with productizing the ROI derived from ML and AI”
Ultimately, the goal of shipping something is to improve the top or bottom line. However, the top and bottom line are lagging indicators. For example, revenue can lag behind usage. So to help maintain agility, you would ship based on metrics for the product (north star, counter) and calibrate these metrics regularly with the top & bottom line.
“Would love to learn or get advice in building an ML/AI product development effort from square one.”
The key difference between ML/AI product development is thinking in terms of Precision & Recall and what is the balance you need to strike to deliver the right customer experience. For example, say you are building a spam classifier to improve email. Would you rather that you have false positives (some spam shows up in inbox) vs. false negative (some non spam messages go to the spam box).
“What is your advice for someone transitioning into Product especially product building for ML/AI product work?”
Spend time to understand the fundamentals of ML. The product you ship is essentially ML models.
“Do you have any final advice for aspiring Product Managers?”
Two pieces of advice for aspiring product managers. First, ensure you do enough research to understand the role. Second, ask yourself why you want to be a PM.