Actively

I'm one of the founders of Actively — our vision is to turn every knowledge worker into a data scientist by building interactive ML tools that combine human expertise with machine intelligence. Here's [more on us](<a href="https://actively-ai.notion.site/About-Actively-9dabd208d3a54d388ea65887745acc0c" rel="nofollow">https://actively-ai.notion.site/About-Actively-9dabd208d3a54...</a>) + a demo of some of the core tech if you're curious :) We're based in NYC and are well-funded by top investors; our (paying!) customers are analytical business teams (think growth, sales, marketing, etc.) at Series C through IPO companies who have tons of questions to answer but very limited access to data scientists. We think of our first product as a "metrics optimization platform" where business users connect to their data, plug in any KPI they care about as an aggregation over that data, and define their levers, and then we continuously optimize that business metric (conversion, churn, upsell, activation, etc.) -- incredibly quickly and without the need for a data scientist. Our thesis is that the two barriers to widespread adoption of AI/ML among business teams are (1) lack of clarity around ROI upfront and (2) the large amount of effort/time required to implement a solution (hire a data scientist, build a bunch of data infra, have them work for a few months, etc.). We solve these by building the right abstractions and human-in-the-loop interfaces to make it incredibly quick for business teams to unlock value from their data -- so quick that we can plug into their data and show them the ROI within a few minutes! We're fairly small (our current team comes from Stanford/Google/Waymo/Scale AI/Microsoft) but are growing the team based on customer demand and looking for entrepreneurial, experienced product and infrastructure engineers who are also excited to work closely with customers + dig in on product & across the business! I'd love to chat: mihir [at] actively.ai! Keywords: AI, ML, machine learning, active learning, explainable AI, causal inference, data science, full-stack, product engineering, data infrastructure