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Namrata Ganatra

Experienced technology leader

Namrata Ganatra, Chief Product & Technology Officer at Pipe

Namrata Ganatra is an accomplished technology executive with over a decade of experience in fintech, payments, and crypto markets. She currently serves as the Chief Product and Technology Officer at Pipe, a modern capital platform for fast, frictionless, dilution-free financing.23

Prior to joining Pipe in May 2023, Namrata held several leadership roles at various companies13:

  • Co-Founder and General Partner at BigBet Ventures (August 2021 - May 2023)
  • Board Advisor at Thrasio (March 2022 - April 2023)
  • VP of Engineering at Thrasio (2021 - March 2022)
  • CTO/VP of Engineering at Autograph (2022 - April 2023)
  • CEO of Storepath AI, an eCommerce growth platform acquired by Thrasio (2020 - 2022)
  • CTO at BloomTech (September 2019 - November 2020)

Namrata has also held engineering and product leadership positions at Coinbase, Meta, PayPal, Stockpile, and Facebook.13

Namrata's educational background includes13:

  • Bachelor's degree in Computer Science from the University of Mumbai
  • Professional Masters in Computer Science from Stanford University (2010-2015)
  • Ignite, Innovation, and Entrepreneurship Program at Stanford University Graduate School of Business (2014)
  • Entrepreneurship Essentials program from Harvard Business School Online (2019)
  • "Women on Boards" program at Yale School of Management (expected completion in 2023)
  • Private Equity: Investing and Creating Value certification from Wharton Executive Education (February 2021)

As Chief Product and Technology Officer at Pipe, Namrata leads the company's product and engineering teams as they work to expand Pipe's modern capital platform for all revenue streams.24 She brings a wealth of experience and expertise to the role, having successfully built and led product and engineering teams at various fintech companies.

Highlights

Aug 26 · linkedin

This is a great technical paper on Pruning and Distillation by NVIDIA

It focuses on 2 models Llama 3.1 (8B parameters) and Mistral NeMo (12B parameters), and successfully compresses them to 4 billion and 8 billion parameters respectively using pruning and distillation techniques.

Pruning reduces the size of a model by selectively removing less important parts like neurons or attention heads without significantly impacting performance.

Distillation process involves training a smaller model to replicate the behavior of a larger model. The smaller model learns from the output of the larger model to maintain performance despite being less complex.

Full paper here 👇

Jun 19 · linkedin

In the evolving B2B landscape, personalization is crucial for growth and retention.

With first-party transaction data, payment companies can develop tailored financial products that resonate deeply with merchants.

In my experience, here are four powerful ways you can leverage data for personalization:

1️⃣ Leverage first-party data for direct, timely, and relevant insights. 2️⃣ Use data to create personalized financial solutions and proactive recommendations. 3️⃣ Implement credit and savings programs that genuinely matter to merchants. 4️⃣ Develop tailored rewards programs to maximize merchant loyalty.

Curious to learn more? My article 👇 has more details on how you can transform your approach to payments.

#pipe #payments #personalization #growth #fintech

Related Questions

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Namrata Ganatra
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Menlo Park, California, United States