Dispute for the sake of Heaven, or why it’s OK to have a loud argument with your co-worker

Any dispute that is for the sake of Heaven is destined to endure; one that is not for the sake of Heaven is not destined to endure
Chapters of the Fathers 5:27

One day, I had an intense argument with a colleague at my previous place of work, Automattic. Since most of the communication in Automattic happens in internal blogs that are visible to the entire company, this was a public dispute. In a matter of a couple of hours, some people contacted me privately on Slack. They told me that the message exchange sounded aggressive, both from my side and from the side of my counterpart. I didn’t feel that way. In this post, I want to explain why it is OK to have a loud argument with your co-workers.

How it all began?

I’m a data scientist and algorithm developer. I like doing data science and developing algorithms. Sometimes, to be better at my job, I need to show my work to my colleagues. In a “regular” company, I would ask my colleagues to step into my office and play with my models. Automattic isn’t a “regular” company. At Automattic, people from more than sixty countries from in every possible time zone. So, I wanted to start a server that will be visible by everyone in the company (and only by them), that will have access to the relevant data, and that will be able to run any software I install on it.

Two bees fighting

X is a system administrator. He likes administrating the systems that serve more than 2000,000,000 unique visitors in the US alone. To be good at his job, X needs to make sure no bad things happen to the systems. That’s why when X saw my request for the new setup (made on a company-visible blog page), his response was, more or less, “Please tell me why do you think you need this, and why can’t you manage with what you already have.”

Frankly, I was furious. Usually, they tell you to count to ten before answering to someone who made you angry. Instead, I went to my mother-in-law’s birthday party, and then I wrote an answer (again, in a company-visible blog). The answer was, more or less, “because I know what I’m doing.” For which, X replied, more or less, “I know what I do too.”

How it got resolved?

At this point, I started realizing that X is not expected to jeopardize his professional reputation for the sake of my professional aspirations. It was true that I wanted to test a new algorithm that will bring a lot of value to the company for which I work. It is also true that X doesn’t resent to comply with every developers’ request out of caprice. His job is to keep the entire system working. Coincidentally, X contacted me over Slack, so I took the opportunity to apologize for something that sounded as aggression from my side. I was pleased to hear that X didn’t notice any hostility, so we were good.

What eventually happened and was the dispute avoidable?

I don’t know whether it was possible to achieve the same or a better result without the loud argument. I admit: I was angry when I wrote some of the things that I wrote. However, I wasn’t mad at X as a person. I was angry because I thought I knew what was best for the company, and someone interfered with my plans.

I assume that X was angry when he wrote some of the things he wrote. I also believe that he wasn’t angry at me as a person but because he knew what was best for the company, and someone tried to interfere with his plans.

I’m sure though that it was this argument that enabled us to define the main “pain” points for both sides of the dispute. As long as the dispute was about ideas, not personas, and as long as the dispute’s goal was for the sake of the common good, it was worth it. To my current and future colleagues: if you hear me arguing loudly, please know that this is a “dispute that is for the sake of Heaven [that] is destined to endure.”

Featured image: Source: http://mimiandeunice.com/; Bees image: Photo by Flickr user silangel, modified. Under the CC-BY-NC license.

Gender salary gap in the Israeli high-tech

A large and popular Israeli Facebook group, “The High-Tech Troubles,” has recently surveyed its participants. The responders provided personal, demographic, and professional information. The group owners have published the aggregated results of that survey. In this post, I analyze a particular aspect of these findings, namely, how the responders’ gender and experience affect their salary. It is worth noting that this survey is by no means a representative one. It’s most noticeable but not the only problem is the participation bias. Another problem is the fact that the result tables do not contain any information about the number of responders in any group. Without this information, it is impossible to compute confidence intervals of any findings. Despite these problems, the results are interesting and worth noting.

The data that I used in my analysis is available in this spreadsheet. The survey organizers promise that they excluded groups and categories with insufficiently few answers, and we have to trust them in that. The results are divided into twenty professional categories such as ‘Account Management,’ ‘Data Science’, ‘Support’ and ‘CXO’ (which stands for an executive role). The salary groups are organized in exponential bins according to the years of experience: 0-1, 1-2, 2-4, 4-7; and more than seven years of experience. Some of the cell values are missing, I assume that these are the categories with too few responders. I took a look at the gap between the salary reported by women and the compensation reported by men.

Let’s take a look at the most complete set of data — the groups of people with 1-2 years of experience. As we may see from the figure below, in thirteen out of twenty groups (65%), women get less money than men.
Gender compensation gap, 1-2 years of experience. Women earn less in 13 of 20 categories

Among the workers with 1 – 2 years of experience, the most discriminating fields are executives and security researchers. It is interesting to note the difference between two closely related fields: Data Science and BI/Data Analysts. The former is considered a more lucrative position. On average, the male data scientists get 11% more than their female colleagues, while male data analysts get 13% less than their female counterparts. I wonder how this difference relates to my (very limited) observation that most of the people who call themselves a BI expert are females, while most of the data scientists whom I know are males.

As we have seen, there is no much gender equality for the young professionals. What happens when people gain experience? How does the gender compensation gap change after eight years of professional life? The situation is even worse. In fourteen, out of sixteen available fields, women get less money than men. The only field in which it pays to be a woman is the executive roles, where the women get 19% more than the men.

Gender compensation gap, more than 7 years of experience. Women earn less in 14 of 16 categories

To complete the picture, let’s look at the gap dynamics over the years in all the occupation fields in that report.

Gender gap dynamics. 20 professional fields over different experience bins

What do we learn from these findings?

These findings are real. We cannot use the non-representativity of these data, and the lack of confidence intervals to dismiss these findings. I don’t expect the participants to lies, neither do I not expect any bias from the participation patterns. It is true that I can’t obtain confidence intervals for these results. However, the fact that the vast majority of the groups lie on one side of the equality line suggests the overall validity of the gender gap notion. How can we fix this gap? I frankly don’t know. As a father of three daughters (9, 12, and 14 years old), I talk to them about this gap. I make sure they are aware of this problem so that, when it’s their turn to negotiate compensation, they are aware of the systematic bias. I hope that this knowledge will give them the right tools to fight for justice.

How to be a better teacher?

If you know me in person or follow my blog, you know that I have a keen interest in teaching. Indeed, besides being a full-time data scientist at Automattic, I teach data visualization anywhere I can. Since I started teaching, I became much better in communication, which is one of the required skills of a good data scientist.
In my constant strive for improving what I do, I joined the Data Carpentry instructor training. Recently, I got my certification as a data carpentry instructor.

Certificate of achievement. Data Carpentry instructor

Software Carpentry (and it’s sibling project Data Carpentry) aims to teach researchers the computing skills they need to get more done in less time and with less pain. “Carpentry” instructors are volunteers who receive a pretty extensive training and who are committed to evidence-based teaching techniques. The instructor training had a powerful impact on how I approach teaching. If teaching is something that you do or plan to do, invest three hours of your life watching this video in which Greg Wilson, “Carpentries” founder, talks about evidence-based teaching and his “Carpentries” project.

I also recommend reading these papers, which provide a brief overview of some evidence-based results in teaching: