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I recently started working at Asana and I was pleasantly surprised how people constantly strive to empower each other and make them feel secure in making decisi

I recently started working at Asana and I was pleasantly surprised how people constantly strive to empower each other and make them feel secure in making decisions and picking up responsibility. Mentorship has been gret as well. It has been by far the best environment I have worked in so far.

pspeter3 8y ago
I'm glad you feel that way!
gspilio 8y ago
A small list of articles for people who would like to read more about life in Asana: https://wavelength.asana.com/work-life-balance/ https://wavelength.asana.com/work-life-balance/ (describes the fractal of work-rest) https://wavelength.asana.com/work-plans-high-level-objectives-roadmap-week/ https://wavelength.asana.com/work-plans-high-level-objective... (how we think about planning) https://wavelength.asana.com/workstyle-aors/ https://wavelength.asana.com/workstyle-aors/ (how we think about responsibility)
- mlthoughts2018 8y ago
  I once completed an intense take-home machine learning test for Asana, and the process of getting feedback for my solution was one of the worst, most political experiences I've ever had while interviewing. I don't know if I got paired with a bad engineer or what, but it was a hugely bad outlier in terms of machine learning interviews.
  - gspilio 8y ago
    I am surprised to hear that. Would you be willing to share more details with me?
    - mlthoughts2018 8y ago
      I actually mentioned it in a Hacker News thread a few years ago, and someone named Jack contacted me from Asana to try to provide feedback by email and help clarify what happened. While I appreciated the extra feedback, and the tone of the communication was reasonably polite, the actual commentary of the feedback seemed unreasonable and totally subjective to me. One of the key issues was that the code test on one hand encouraged someone not to spend too long, and to choose a simple statistical model and work on explaining how it works, but on the other hand the reviewer rejected it because it wasn't "results-oriented" or "focused enough on final conclusions." It seemed obvious that given the nature of the take-home test, nobody was looking for a model that definitively solved the problem or had amazing accuracy. Just something small-scale, but with a meaningful discussion of the trade-offs and basic discussion of how you fit a model, diagnostics, items you would work on if given more time, etc. But the feedback I got was that my approach needed to be more "results focused" and that in my write-up I was overly cautious to say not to draw any significant conclusions from a simple model on the toy data set or to read too much into a model that needed to be completed in a couple of hours. They said someone "couldn't thrive" in the Asana environment if they weren't able to work in a more results-driven and conclusion-driven way. I felt the criticisms were extremely subjective and political. As a candidate, I can't read someone's mind about how "results oriented" a vague take-home modeling assignment needs to be, let alone that I have limited personal time which I can devote to it anyway. I sank a lot of time into writing code for a reasonable "first attempt" kind of model, and providing some diagnostic charts and discussion. It felt like Asana really wasted my time on it, and that in the end the feedback reflected some unspecified and vague properties that couldn't be inferred or understood from the test instructions or talking with any of the engineers. It was just random. My same solution for the same test at another company might have been viewed as a great one, even from a "results-oriented" point of view because it would be unreasonable to expect that from a short take-home assignment. I actually got a job as a deep learning engineer not long after that, and then got my most recent job as a machine learning team manager. My experiences working on machine learning models in production and hiring engineers for my team has only reinforced to me that such a manner of take-home data science project is really a bad way to do things.
      - erinakinci 8y ago

pspeter3 8y ago

I'm glad you feel that way!

gspilio 8y ago

A small list of articles for people who would like to read more about life in Asana: https://wavelength.asana.com/work-life-balance/ https://wavelength.asana.com/work-life-balance/ (describes the fractal of work-rest) https://wavelength.asana.com/work-plans-high-level-objectives-roadmap-week/ https://wavelength.asana.com/work-plans-high-level-objective... (how we think about planning) https://wavelength.asana.com/workstyle-aors/ https://wavelength.asana.com/workstyle-aors/ (how we think about responsibility)

mlthoughts2018 8y ago
I once completed an intense take-home machine learning test for Asana, and the process of getting feedback for my solution was one of the worst, most political experiences I've ever had while interviewing. I don't know if I got paired with a bad engineer or what, but it was a hugely bad outlier in terms of machine learning interviews.
- gspilio 8y ago
  I am surprised to hear that. Would you be willing to share more details with me?
  - mlthoughts2018 8y ago
    I actually mentioned it in a Hacker News thread a few years ago, and someone named Jack contacted me from Asana to try to provide feedback by email and help clarify what happened. While I appreciated the extra feedback, and the tone of the communication was reasonably polite, the actual commentary of the feedback seemed unreasonable and totally subjective to me. One of the key issues was that the code test on one hand encouraged someone not to spend too long, and to choose a simple statistical model and work on explaining how it works, but on the other hand the reviewer rejected it because it wasn't "results-oriented" or "focused enough on final conclusions." It seemed obvious that given the nature of the take-home test, nobody was looking for a model that definitively solved the problem or had amazing accuracy. Just something small-scale, but with a meaningful discussion of the trade-offs and basic discussion of how you fit a model, diagnostics, items you would work on if given more time, etc. But the feedback I got was that my approach needed to be more "results focused" and that in my write-up I was overly cautious to say not to draw any significant conclusions from a simple model on the toy data set or to read too much into a model that needed to be completed in a couple of hours. They said someone "couldn't thrive" in the Asana environment if they weren't able to work in a more results-driven and conclusion-driven way. I felt the criticisms were extremely subjective and political. As a candidate, I can't read someone's mind about how "results oriented" a vague take-home modeling assignment needs to be, let alone that I have limited personal time which I can devote to it anyway. I sank a lot of time into writing code for a reasonable "first attempt" kind of model, and providing some diagnostic charts and discussion. It felt like Asana really wasted my time on it, and that in the end the feedback reflected some unspecified and vague properties that couldn't be inferred or understood from the test instructions or talking with any of the engineers. It was just random. My same solution for the same test at another company might have been viewed as a great one, even from a "results-oriented" point of view because it would be unreasonable to expect that from a short take-home assignment. I actually got a job as a deep learning engineer not long after that, and then got my most recent job as a machine learning team manager. My experiences working on machine learning models in production and hiring engineers for my team has only reinforced to me that such a manner of take-home data science project is really a bad way to do things.
    - erinakinci 8y ago