Ritvvij Parrikh

About Ritvvij Parrikh

18 years experienced, 2x entrepreneur turned outcome-driven operator in AI- Problem Solving: I\'m a deeply technical product manager who can turn AI theory into repeatable outcomes from 0–1 (feasibility, pilots) to 1–10 (scaled deployment)- P&L: GTM TOI+ from zero to 120k subscribers in year 1. Bootstrapped two profitable businesses. Have sold to companies like Meta, Gates Foundation, ICICI Lombard, etc- People: Have led cross-functional teams (data scientists, product managers, editors, designers, data engineers). Peak direct report 20 people. Can hire from networks- High Stakes Communication: Can articulate the company’s vision, further the company’s interest and responsibly handle scrutiny. I’ve participated in sense-making and problem-solving brainstorming with CXOs and owners. Bylines in Neiman Labs (Harvard), Times of India, INMA, and Times Internet’s Product Blogs- Entry/Exit Judgment: Strategically folded two businesses at peak cash position. Angel invested in a LLM-based analytics firm (YC W23) prior to the LLM revolution.

Experience

1. Senior Director of Product Management Ai

   Times Internet

   Jun 2022 — Present

   Traffic concentrated in SEO; Tasked to grow engagement and retention of TOI\'s owned platforms; AI we ship is seen by audiences without humans in the loop (requires high accuracy). Personalization doubled feed CTR & grew retention by 10%.Machine Learning- In-house data wasn\'t reliable; Built CDP, redid data pipeline, and drove adoption; We now process 100m events daily, and warehouse stores 750 GB. CDP is the backbone for ML- Website & apps designs added bias into data; Collaborated to redesign TOI Apps- Website & apps can\'t serve ML; Worked with engg. to drop cache & use servers to render- News is low ARPU business and ML is costly; Cost-modeled ML and optimized API to 195ms- Stakeholders doubted if ML can run audience-facing tasks; Shipped transparent dashboards in Metabase & Kibana to track outcomes and ML Ops- Media struggle with retention ‘coz they run many revenue models at the same time; Proposed a method to mix ML with P&L to maximize revenue within target retention.Generative AI- Even if outcomes are high, does the feed pass the smell test? Increasingly building evals (labeled data) to track if the right stories are amplified. Using LLM to build new features- LLMs can summarize but in doing so they lose news peg; Building agents to catch news peg — why a story exists and using it to rewrite headlines to drive engagement- Piloted chatbot inside of TOI Apps to test their reaction to chat as an interface- Conceptualized Context Cards — Amazon X Ray but for news- Stack: LangChain, DSpy, GPT APIs, Perplexity APIs, Pine Cone, Embeddings

Education

• Continued Learning

  -, -

  2012

• University of Mumbai

  Bachelor Of Engineering, Information Technology

  2003 — 2006

Skills

• Management
• Analytics
• Social Entrepreneurship
• Start-Ups
• Entrepreneurship
• Leadership
• Saas
• Data Visualization
• Business Development
• Cloud Computing
• Software Development
• Crm
• Marketing
• Cross-Functional Team Leadership
• Product Management
• E-Commerce
• Integration
• Data Analysis