Research trained me to look twice.

The industries look different on paper. The common thread is how I work: define the question, find evidence close to the real behavior, make uncertainty visible, and turn the finding into a decision someone can act on.

That can mean interviewing people who left a funnel, walking through seventeen competitor products myself, translating a policy into system behavior, or defining success metrics before an MVP exists.

• Research without theatre. I use the lightest method that can answer the question, and I report limitations alongside conclusions.
• Structured ambiguity. I turn scattered constraints, stakeholder needs, and operational details into a shared artifact.
• Commercial awareness. Product choices include unit economics, vendor reliability, continuity, and ongoing operating cost.
• Constraint-led design. Privacy, policy, and customer protection belong in the product model—not in a final review checklist.
• AI-assisted, human-decided. I use multiple models to explore and challenge work, but evidence, judgment, and accountability stay with the PM.

I am currently completing Product Management with Generative & Agentic AI at BITS School of Management. I am interested in how AI changes discovery, product operations, evaluation, and the shape of software itself—without pretending every problem needs a model.

Earlier, I completed an M.Tech in Bioinformatics with a 9.33/10 CGPA and a B.Tech in Biotechnology. That background still shapes my instinct to distinguish a plausible explanation from a supported one.

Product Manager opportunities where I can own a meaningful problem, work close to users and engineering, and be judged on the quality of decisions as well as output. I am particularly interested in AI products, B2B SaaS, and thoughtful consumer experiences. I am based in Bengaluru and open to remote teams.