Oct 2021 – Present · 4 yrs 7 mos · Mumbai, Maharashtra, India

• Co-founded a regulated wealthtech product for India’s conservative affluent segment. Built a 120K organic community in a trust-sensitive category. Exited via acquihire.

Apr 2020 – Sep 2021 · 1 yr 5 mos · Mumbai, Maharashtra, India

• Kapiva is an Ayurvedic D2C brand. I ran the entire online business with P&L ownership across marketplace.
• Grew revenue multi-fold in 18 months by rebuilding channel architecture: catalog strategy per platform, search optimization per marketplace, pricing logic that protected margins while winning the Buy Box, and a content engine that turned Ayurvedic ingredients into stories people clicked on.
• Built ads infrastructure from scratch across Amazon AMS, Flipkart PLA, Google Shopping, and Meta. Every rupee tracked to ROAS. Every SKU mapped to contribution margin.
• If AI existed then: this is where it gets painful. Scraper could have tracked every competitor's pricing, ranking, and ad position across marketplaces in real time. Helium 10 with GPT plugins would auto-generate listing copy and A+ content. Perpetua or Pacvue would run ads autonomously, adjusting bids by hour on conversion velocity. ChatGPT could write 500 product descriptions in a day. A week of catalog work becomes one prompt. Live dashboard on Looker with an LLM writing SQL. Klaviyo with AI subject lines and send-time optimization. An AI agent watching purchase frequency, triggering offers before churn. The entire 4-platform operation could be one person with the right stack. We did it with spreadsheets and too many tabs.

Jul 2019 – Apr 2020 · 9 mos

• FoodWork was Zomato's enterprise cafeteria product. 400 cafeterias. 4M meals served per month. I got promoted to AVP Growth in 7 months.
• The job was to figure out how a food-tech company makes money from corporate campuses beyond commission. So I redesigned the entire growth model: brand partnerships inside cafeterias, activations on campus, and in-app monetization channels that nobody had tried before. Revenue grew 25% and hit $6M annually. Built the support, onboarding, and product-ops systems from scratch. NPS went from -5 to +20. Onboarding time dropped 20%. Cafeteria sign-ups grew 10% month on month.
• If AI existed then: An agent would have scored every cafeteria lead by employee count and revenue potential, auto-personalized onboarding decks by campus size, predicted churn 30 days out from usage patterns, run A/B tests on activation flows overnight, and generated partnership proposals on its own. The entire growth surface I managed could have been a workflow in Clay with enrichment APIs and a sequencing tool.
• Instead, we had Google Sheets, weekly standup calls, a Slack-like channel that never slept, and an unreasonable amount of coffee. The results were the same. The sleep was not. But I learned something an AI still cannot teach you: how to read a room full of skeptical cafeteria managers and convince them that this weird food-tech startup was worth their lunch hour.

Dec 2018 – Jul 2019 · 7 mos

• Post-merger integration, Zomato x Tonguestun. Two companies, two cultures, one product to stitch together.
• If AI existed then: compliance mapping would have been an afternoon with an LLM, client migration a drag-and-drop workflow, and ops playbooks auto-generated from both company wikis. We did it in months, manually, one process at a time.

Sep 2016 – Jun 2017 · 9 mos

• Mumbai hub. $XXM annual business. 400 hotels. 20 managers. Won Best Operations Head across 19 hubs.
• If AI existed then: real-time quality scoring per hotel, automated partner comms triggered by guest reviews, and predictive staffing models that adjust by season. We built data-backed strategies by hand and trained teams on the ground, hotel by hotel, lobby by lobby.

Sep 2015 – Aug 2016 · 11 mos

• Led key operational projects focused on improving service quality and efficiency across OYO's network.
• Led the launch strategy for OYO Care, an on-demand cleaning service for hotel partners
• Managed business development, vendor management, and service delivery quality assurance
• Led the Rooms audit app launch Project, classifying rooms inventory across 6000+ hotels into Black (non-compatible) or Green (compatible) categories
• Oversaw product management, project management, and data analytics for the project

Oct 2014 – Sep 2015 · 11 mos

• OYO was a 30-person startup when I joined. By the time I left this role, it was in 200+ cities. My job was to make sure the expansion did not happen blind.
• Built the analytics function from zero. No team, no dashboards, no data culture then. Hired 8 analysts. Set up the entire data infrastructure: tracking, reporting, self-service tools, and the weekly metrics cadence that leadership used to make every expansion call.
• The biggest thing I built: a city expansion tool. It scored every potential city in India on supply density, demand signals, competitor presence, and revenue potential. That tool became the backbone of how OYO decided where to go next. 1 city to 200+. Not random. Data-picked.
• Also built the self-service analytics platform so that ops teams across the country could pull their own numbers without filing tickets. Reduced analytics dependency by 60%. Made the team a force multiplier instead of a bottleneck.
• The daily reality included stitching together data from 15 different sources, cleaning hotel-level metrics that were entered manually by field teams, building models on spreadsheets because the data warehouse was a work in progress, and presenting insights to founders who wanted answers yesterday.
• If AI existed then: an LLM agent could have ingested every hotel listing, review, and competitor price in real time. City scoring would have been a live dashboard, not a quarterly model. Self-service analytics would have been a chatbot, not a portal. The expansion tool would have updated itself daily instead of monthly. And the 8-person team? Probably 2 people with better tools.
• But here is what I learned that no AI replaces: how to build a data culture inside a company that had never had one. How to convince ops leaders that numbers matter more than gut feel. How to hire analysts who think in business outcomes, not SQL queries. That playbook still runs in my head every time I set up analytics for a new company.

Jun 2013 – Sep 2014 · 1 yr 3 mos · Bengaluru, Karnataka, India

• Flipkart First Launch | Mobile App growth strategy | Working Capital Management across 20+ categories

May 2012 – Jul 2012 · 2 mos · Greater Delhi Area

• Part of sell side M&A advisory team during summers 2012
• Performed due diligence on South African coal industry and produced a high quality report that was used as a pitch to prospective clients
• Constructed (from the scratch) a smart user friendly BI tool that disseminated curated content for higher management to strategise upon
• Work appreciated by the MD and received a PPO to join the firm

May 2011 – Jun 2011 · 1 mo · Bengaluru, Karnataka, India

• Worked on multiple projects during summers 2011
• Modelled credit risk elements under Basel II norms using PD and LGD methodologies
• Worked with UK based business engineering team to perform RCA of the increasing cost and operational risk of the derivatives trade capture process by creating value stream maps.Identified non value add items that blocked around 8 Full time equivalents capacity (performed capacity modelling to arrive at this figure)