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 📈

  • My Code
  • Marketing & Advertising
  • United States
Rank
#351of 18,993
  • 11.0K followers
  • 19 posts / 60d
  • 1.2% avg engagement
  • 150 follower growth / 30d

Nikhil Mehra ranks #351 of 18,993 creators on the LinkedIn Ranking, working in Marketing & Advertising and based in United States. They have 11.0K followers and published 19 posts in the last 60 days at a 1.2% average engagement rate.

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

  • Maybe we don’t have a learning problem. We have an application problem. Bad way to learn: • Save every article • Watch every course • Keep up with every new AI tool • Apply almost none of it Better way: • Start with a real problem • Learn what helps solve it • Apply it immediately • Repeat Knowledge is abundant. Application is the advantage. #ArtificialIntelligence #ProductManagement #Leadership #ContinuousLearning #Innovation

    51 reactions · 15 comments · 0 reposts

  • Here's one thing that took my personal RAG customer support bot from hallucinating to highly accurate once I stopped trusting pure vector similarity: 🛑 I restricted the retrieval pipeline and forced it through a rigid metadata router first. 🗂️ 🚫 No massive 100k-document flat vector database. 🚫 No tweaking embedding models for weeks. 🚫 No relying on a massive LLM to "figure out" the context from a bloated prompt. Instead, I just did this: 1️⃣ Tagged every support doc with strict metadata (product tier, issue type, user persona). 2️⃣ Built a lightweight, zero-shot classifier to route

    70 reactions · 8 comments · 0 reposts

  • My team wasted $4,000 on AI last quarter. It was just supposed to summarize meetings. Dash told the AI to "write the best action plan" but forgot to mention our budget cuts and past failures. Nobody noticed. Because we only tracked "reports generated." The number went up. Everyone was happy. But 3 out of 5 AI strategies recommended projects we'd already canceled. The mistake was mine. I built a system that rewarded speed over smarts. So we changed one thing: AI suggests, humans decide. Think Google Maps. It shows the route, but you still drive. Our new setup: 1) AI drafts, humans approv

    68 reactions · 7 comments · 1 reposts

Last updated 2026-08-01