In a very candidate-short market, companies are competing to attract talent. From experience, we know a positive candidate experience is key to be able to have a chance to beat this competition.

During this talk, I will share my experiences as a recruiter helping Tokyo-based companies to hire engineers, including examples of what worked, and what didn't.

Most people already know that the tech industry looks incredibly homogeneous and lacks diversity in every layer of it. But the contributing factors and the consequences of this picture are so hard to measure that it's still rare to have an honest discussion about the importance of diversity and reach an agreement on what can be done about it or if anything should be done at all. This talk brings a complete overview and tons of data on underrepresented groups in tech, the causes of underrepresentation, why the lack of diversity is a problem in the first place, the impact on your business and how important we, as leaders, are to improve this picture. The numbers are mostly focused on (the lack of) women in the tech industry, but many examples apply to other underrepresented groups and even other industries.

Crafting interviews is hard. How many interviews do you do? Do you do live coding? Are questions the same each time? Especially in small companies, the interview process can be informal, inconsistent, and -- worse -- ineffective at getting offers to the right people. Chris will describe how Bespoke revamped their interview process to optimize for efficiency and happiness, helping them grow their engineering team from 6 engineers to 15 in the first half of 2019. Furthermore, she'll share a general methodology that can be applied at any company wishing to redesign their interview process, customized for their company values. This talk is suitable for any hiring manager or individual with influence over the hiring process, especially for technical teams.

This is a talk borne from experiences of the many ways that Key Performance Indicators can go wrong. We will try to answer what 'key' means, what 'performance' means, and when something actually can be considered an 'indicator'. We'll look at amusing incentives, try to separate signals from the noise, and once and for all understand the difference between accuracy and precision.