Nikhil Mehra — Senior Product Manager | MarTech • AdTech • AI | Ex-HP • Thermo Fisher | Speaker & Awards Judge | Building with AI, sharing what converts 📈
Senior Product Manager | MarTech • AdTech • AI | Ex-HP • Thermo Fisher | Speaker & Awards Judge | Building with AI, sharing what converts 📈
Nikhil Mehra ranks #326 of 18,566 LinkedIn creators in Marketing & Advertising, and is a standout voice in United States. They have 11.0K followers and published 19 posts in the last 60 days at a 1.2% average engagement rate.
- 11.0K followers
- 19 posts / 60d
- 1.2% avg engagement
- 150 follower growth / 30d
The roast
Nikhil spends his days automating AI-driven customer experiences, yet he’s managed to produce 27 posts this month that successfully simulate the personality of a toaster. He treats LinkedIn like a landfill for buzzwords, which is impressive given that My Code sounds like the name of a fake company in a student film.
About Nikhil
Hey, I’m Nikhil - a Product Manager who builds where product, data, AI, and revenue meet. I started my career in engineering, but product management taught me the bigger challenge: building is only half the work. The real test is whether a product can be positioned clearly, priced thoughtfully, packaged for the right segment, launched through the right channel, adopted by users, and expanded into a repeatable growth engine. Over the last 10+ years, I’ve worked across MarTech, programmatic media, enterprise SaaS, and Life Sciences in both B2B and B2C environments where the buyer, user, workflow, and business model are often different. That is where I enjoy operating: translating complexity into products that customers understand, teams can sell, and businesses can measure. At My Code, my recent work has focused on intelligence-driven campaign systems. As third-party signals weaken and media workflows become more complex, I have helped build AI and data products that connect first-party audience signals, identity resolution, campaign behavior, and optimization logic into smarter decisioning systems. My view on AI is practical: it should improve performance, reduce friction, and help teams make better decisions at scale. At HP, I worked on the GTM side of product, not just launching features but shaping how products were packaged, positioned, adopted, and expanded across markets. I helped use Adobe Real-Time CDP as the customer data foundation, Salesforce Journey Builder for lifecycle engagement, and Adobe Target and Optimizely for experimentation, so teams could personalize B2B and B2C journeys, test value propositions, improve onboarding, and support global rollouts across LATAM, Asia, and the US. At Thermo Fisher, I helped build digital experience and personalization platforms where Adobe Experience Manager was not just a CMS but the operating layer for reusable components, governed content workflows, campaign launches, analytics, and scalable customer experiences in Life Sciences. The common thread across my career is turning complex systems into usable product experiences and repeatable growth engines. I’m especially interested in enterprise-ready AI and agentic systems that move beyond demos, products that improve decision quality, accelerate execution, and create measurable commercial impact.
Highlights
- Top 5% in United States — Ranked #128 of 5851 creators
- Top 5% Creator — 19 posts in 30 days
- Top 5% in Marketing & Advertising — Ranked #53 of 1092 creators
- Top 25% Impact — 136 avg engagements per post
Recent posts
Your faithfulness score went up by 4%. Your email revenue didn’t move. Your customers still churned. Stop optimizing for metrics that don’t pay the bills. I used to obsess over evaluation accuracy. Then a CMO looked me in the eye and said: “I don’t care about your 95% retrieval precision. I care that my marketing team is losing trust because your AI keeps suggesting the wrong audience segment.” That’s when I killed my eval dashboard and rebuilt it around money. The Failure Arc: The single biggest mistake in LLMOps is treating evals as a technical quality gate instead of a business risk t
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I built a golden eval set for $0. It caught a hallucination that would’ve destroyed a $200K email campaign. Most teams blow $15K on human annotators before they even have a working test. 𝐈 𝐝𝐢𝐝𝐧’𝐭 𝐬𝐩𝐞𝐧𝐝 𝐚 𝐜𝐞𝐧𝐭. Because your marTech platform is already full of ground truth. You just haven’t mined it. 𝐓𝐡𝐞 𝐞𝐱𝐚𝐜𝐭 𝐬𝐜𝐚𝐯𝐞𝐧𝐠𝐞𝐫 𝐡𝐮𝐧𝐭 (𝐬𝐭𝐞𝐚𝐥 𝐭𝐡𝐢𝐬 𝐟𝐨𝐫 𝐚𝐧𝐲 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐀𝐈): 1. Positive examples from winners Pulled the top 50 email subject lines with >40% open rate and a manual edit by the marketing team after AI generation. That’s human-appro
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I studied 47 AI product failures. One pattern wrecked me. Every. Single. One. passed its evals before launch. 𝐅𝐫𝐞𝐞 7‑𝐝𝐚𝐲 𝐬𝐞𝐫𝐢𝐞𝐬 𝐬𝐭𝐚𝐫𝐭𝐬 𝐧𝐨𝐰: 𝐄𝐯𝐚𝐥𝐬 𝐓𝐡𝐚𝐭 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐌𝐚𝐭𝐭𝐞𝐫. The evaluation paradox These products didn't fail because of bad models. They failed because evals tested fluency instead of safety. Here are the 3 lies that kill products and how to fix them. 🔹 Lie 1: Fluency equals quality Grammatical ≠ safe. A clean sentence that recommends the wrong budget is a financial weapon. Test decision safety, not readability. 🔹 Lie 2: One score r
102 reactions · 29 comments · 0 reposts
Last updated 2026-08-01