Remote Tech Job Interview Cheat Sheet

Hacker News

Hey Hackers, Yura and Nikita here: we're the founders of YouTeam (W18). Today we present a simple customizable cheat sheet for you to quickly asses the soft skills of your remote engineering candidate: https://youteam.io/remote-developer-interview-tool Everyone who ever had to interview a job candidate knows - these interviews suck! You only have an hour or so to make a decision that will have a profound impact on your business and may potentially cost you tens of thousands of dollars. The trickiest part is assessing the soft skills: attitude, cultural fit, values. Is this the "right" person for your team? This question becomes even more difficult if the candidate isn't sitting in the same room with you and all you have is a blurry Zoom video. Which becomes a new 'normal' nowadays. Wise men say - 'preparation is the key'. Yet in practice, it turns our tricky to allocate dedicated time to meticulously prepare for each interview. Few of us who are that perfect usually work for British Intelligence. At YouTeam, we interview hundreds of engineers routinely - part of our standard vetting process. When you do something on a regular basis for years - certain patterns start to emerge. You suddenly see what works and what doesn't, which questions give you key insights and which are useless. Today we decided to share what we learned for over 3 years with the world - in a form of the customizable cheatsheet. Usually, you only have time for 10-15 questions - so we made this easy to pick only what you deem relevant. The entire process takes just a couple of clicks - so if you didn't get to prep in advance (just like as most of us) - this tool can be your quick last resort. Looking forward to hearing your feedback on how this cheatsheet worked in the field and how we can make it better. Thanks - and stay healthy!

Visit product View on Hacker NewsMay 18, 2020

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

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best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, para · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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