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Tristan Tao

Building Complete Memory AI+BI | UC Berkeley & YC S15

Santa Monica, California

Work Experience

2022

  • Co-Founder & CEO

    2022

    Venture-backed stealth mode.

  • Venture Partner

    2017

    Investor and Advisor. Fund I and III.

2019

  • Angel Investor

    2019

    Rain.AI SuperMove Rora, Inc Edge (payment) answersai.com Various stealth

2017 - 2022

  • Advisor

    2022 - 2022

    Continued consultation for the organization I left behind, as well as advisory to the CEO on topics including GM and M&A.

  • Vice President of Solutions

    2020 - 2022

    I helped scale GTM @ Iterable for pre and postsales from $5m to $120m+ ARR over 5 years by owning functional areas including: - Solutions Consulting - Solutions Architecture - Value Consulting - Demo Engineering Some additional responsibilities include: - COGS / margin / pricing - Analyst relations (Forrester / Gartner) - Drove early-stage product roadmap (Series B/C) - Late-stage fundraising (Series C/D/E)

  • Senior Director, Solutions

    2019 - 2020

    Leader of our pre-sales Solutions Consulting teams and post-sales Solutions Architect team spanning SF, Denver, NY, and UK.

  • Director of Solutions Consulting

    2017 - 2019

    Leader of our pre-sales Solutions Consulting team.

  • Sales Engineering Lead

    2017 - 2017

    First one in the arena.

2021 - 2021

  • Leadership Council Member

    2021 - 2021

    I had the honor of serving on the inaugural leadership council for PreSales Leadership Collective over an annual term.

2014 - 2019

  • Co-Founder & CTO (YC S15)

    2014 - 2019

    Product | Engineering | Sales Leada is a venture/Y Combinator backed online education Saas startup based in SF. We are trusted by some of the best universities around the world. Tech - Continuously design, build, and iterate on the product (https://www.teamleada.com/) based on user feedback. - Ran A/B test to drive ~3x increase in demo requests. - Oversaw migration from Heroku to AWS, leading to $15k in quarterly cost saving. - Manage deployments on AWS to ensure 99.999% uptime. Sales / Marketing: - Built out a repeatable inside sales process. - Grew lead generated by 5x in less than 3 months with zero added cost. - Closed $100k+ in sales. - Development of (http://analyticshandbook.com/ 50k+ downloads) to drive organic traffic. Recruiting / Management: - Recruit and manage SDR/lead gen to build a predictable pipeline - Recruit and manage a software delivery team that led to high CSAT with customers like Zenefits, Twitch, and TeeSpring. - Recruit and manage curriculum developers for Python and SQL courses, which became our largest enterprise revenue driver. - Manage engineers and contractors to ensure agile feature delivery. Full time until Jan 2017, part time after.

2016 - 2017

  • Business Development

    2016 - 2017

    Pioneered partnerships with industry leaders including Deloitte, Wipro, and the Auto ISAC. This was a special short term role reporting directly to the CEO.

2016 - 2016

  • Interim CTO

    2016 - 2016

    YCS15 batchmate. (Acq. by Glam&Go in 2017). Interim CTO to lead technical projects including: - Successfully plan and implement a full Braze Integration - Successfully plan and implement FriendBuy referral integration - Technical debt payoff via documentation/test plans - Advise the CEO on tech stack

2013 - 2013

  • Software Engineering, Security and Compliance Solutions

    2013 - 2013

    Built a full featured Splunk app that allows for consumption, visualization and analysis of Cisco NetFlow data exports. Some features inculde: • Predictive analytics of various network traffic utilizing machine learning algorithms (built on top of BSD-licensed 3rd party python libraries). The algorithms offered better prediction than the default built-in Splunk “predict” command which leverages kalman filters. • Fast visualization of GB of data through Splunk’s experimental tool, tsidx. • Continuous real-time network monitoring capability.

2012 - 2013

  • Data Science

    2012 - 2013

    • Designed and built a company popularity ranking model through text sentiment analysis. The project required analysis of 200GB+ of unstructured text data as well as structured intra-day text click data. The tools used include Hadoop (MapReduce as well as custom-built UDAF on Hive), R, Java, Open Calais (for NLP), and Python. The model performed better than the existing model in several metrics. • Improved automation and monitoring by creating automated server redeployment and backup tools via Fabric. It came in handy when I torched a production server. Valuable lessons were learned. • Maintained and upgraded legacy code.