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      Numa

      Engaged employer

      Numa reviews

      Incredible Company, Even Better Leadership - Senior Director, Revenue Operations Numa Employee Review

      5.0
      Apr 7, 2025
      Senior director, revenue operations
      Recommend
      CEO approval
      Business outlook

      Pros

      Company is run by an incredible leadership team that has "done it before" (successful exit). I admit I have a bias here as I bore witness to the last exit - but I've never worked for leadership who care this much about their people (I've been at 5 companies since). I was more than thrilled to come back and work for this team. This results in real support from executives and leadership, a willingness to invest in people and the tools they need to succeed.

      Cons

      We're growing fast! This means every day can be different. (Not always a con, but it can be perceived as such for those seeking day-to-day functional stability or "big/long-term projects".

      Explore other reviews about Numa

      5.0
      May 6, 2024
      Anonymous employee
      Recommend
      CEO approval
      Business outlook

      Pros

      Exciting and disruptive technology Great team to work with and visionary leader Solid compensation and benefits package Everything you expect from a startup

      Cons

      Mostly remote, miss the opportunity to work in an office

      5.0
      May 1, 2024
      Data scientist engineer
      Recommend
      CEO approval
      Business outlook

      Pros

      Forward thinking company with genuine people and strong leadership/founders. Lots of real customer data to work with and interesting problems to solve. Everyone is approachable and collaborative. Leadership values workplace culture and flies everyone out for on-sites a few times a year to help strengthen the team. Not afraid to put new things in front of customers to get real feedback/iterate quickly on approaches. Team members get to be a part of developing the roadmap and propose new solutions.

      Cons

      As a startup, there is not a dedicated ML-Ops team or taxonomists to annotate data which requires folks to "wear multiple hats" and can lead to slower progress from the model development side. (Could be considered a pro).

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