My last post was about one of the bugbears of data scientists, the thrash. Another hobgoblin is the somewhat toxic manager, coworker, or workplace. There’s a lot of content out there already about toxicity in the workplace and what to do about it. I’ll try to point out what’s different in data science and offer my somewhat cynical take on things.
Toxicity is much more of an immediate and concrete problem than the thrash. The issue is software engineer Steve and how you can get through the team meeting without a) quitting on the spot, b) slashing Steve’s tires in the parking lot after work, or c) feeling awful and eating a pint of frozen hot cocoa ice cream when you get home. The Steve problem is easy to explain to coworkers, friends, and family. They will empathize. They will also quickly tire of the conversation. Talking about the issue can make you look weak or like a gossip. This is especially true if whoever you are talking to likes Steve or Steve’s work. Or if Steve is not actually all that toxic. It’s complicated, at least in data science.
Let me say up front that I don’t believe I’ve worked with any truly toxic people. I believe such people are rare in data science. My partner is an Occupational Therapist (OT). All of the OTs I have met have been kind. There must be some toxic OTs but it’s honestly hard to picture that. I spent most of my professional career in academia and at a think tank. Toxicity is relatively common there. Narcissism and all. Even so, I’m not sure I worked with any truly toxic people. There was a lot of awkwardness and bad management, sometimes coming from me. (Why is there no management training in research – or data science?) I did hear awful stories about professors or collaborators I barely knew. The stories generally focused on the PhD student — advisor relationship. Codependence and a strong power imbalance breed toxicity.
Do we have such conditions in data science? It’s rare. I have noticed that junior data scientists working directly with people much higher up in the org chart, often in the C-suite, are typically the ones who experience toxicity. Truly toxic people, to the extent they exist in the field, will target those with less power. Junior data scientists will also have less opportunity to fight or flee or ignore when faced with problematic situations. The role of a junior data scientist can be similar to that of a PhD student. The fact that there are so many people trying to be junior data scientists right now doesn’t help the situation. I would say to be choosy when taking your first data science job or two, but I know lots of people don’t have that luxury.
The junior data scientist is more likely to inadvertently say or do something that will trigger an outburst. The new hire might not know when to ask for help and when not to ask, when to wait and when to push ahead with analysis, and just how to be thought of as a high performer. He or she might not have the context to know what the company has tried 37 times before without success, or what sets software engineer Steve off. The new hire is also more likely to do bad data science. The new hire is less likely to have seen aggressive coworkers. Insert joke about sensitive millennials here but it was shocking to me, at age 39, when I got my first tech job and ran into my first aggressive coworkers. There are no adults in the room at many tech jobs.
I don’t believe I’ve worked with any truly toxic people, but I have worked with people who took it upon themselves to organize their coworkers and to critique the efforts of others particularly during team meetings. I did work at Uber, famous for corporate values such as toe-stepping and principled confrontation. It probably sounds like I’m basing this article on Uber, but honestly it wasn’t that different at my other tech jobs.
All teams in tech are dysfunctional to one degree or another. There is lots of bad data science being done. Both can limit the effectiveness of otherwise solid contributors and teams. Both can be infuriating. Especially when deadlines are tight and expectations are high. Especially to those trying to climb the corporate ladder quickly. Especially to those who view themselves as teachers or leaders. This is what I’ve seen in data science. People who had good intentions but who would occasionally toe step… or worse. Many of these people have been, or would be, surprised to learn that they were called toxic. Many of these people were after a promotion, or three. They essentially all got those promotions. More on this later. If I sound like an apologist, keep reading.
There are aggressive people in other domains too, of course. I can remember being shocked at how intense and fierce the Berkeley IEOR professors were to visiting faculty giving talks. They really did not like fuzzy logic. This is apparently common at top technical departments. Were they toxic? Not sure I’d go that far. I will say that the very smartest and most accomplished professors I have interacted with, the kind that manage to play in the symphony orchestra when they’re not teaching, have also been amongst the kindest.
The difference in data science is proximity. You, especially if you are a new hire, will have to work closely with software engineer Steve. In the before times, this was literally true. Somehow it’s even worse now. Maybe because you don’t see all of Steve now, just the side of him where he’s yelling at you. The zoom call or slack ding can be anxiety inducing.
The (slightly) toxic manager or coworker(s) problem in data science disproportionately affects women and introverts. These groups are, generally speaking, less secure and confident in the workplace. There is surely a relationship to race, ethnicity, and national origin as well. I haven’t seen anything remotely relevant on the job, thankfully, but I know the relationship is there. The Robert Sarver story, a case study on toxicity, is really a story about race and gender. There is nothing special about data science in this regard, except maybe that the demographics are different.
The (slightly) toxic coworker problem is made worse by a feeling that the coworker is part of a powerful clique maybe even including the manager. You can see how this would be worse for the marginalized.
People in a work clique may not think of themselves as part of a clique. I remember a coworker at Uber (who was by no means toxic) sitting somewhere new in the office for one day and realizing that the crew around where he normally sits looked and sounded like The Plastics. Those of us on the outside knew this already. The open plan startup office is the high school cafeteria. Want to ensure a good work environment for all? It can help to shake up the seating chart or the team meetings. On the other hand, a solid work clique is exactly what’s needed to rapidly produce solid new features and products.
That last line is exactly why the manager might be a part of a clique or support the clique. We can also go back to what I wrote earlier, that in data science there are more toe steppers and less outright toxic people. The toe steppers are trying to pull their team up. Managers often see this and appreciate it. These toe steppers are often high performers or at least thought of as high performers. They have often been in the organization a long time and are high up in the org chart. So they interact with the managers frequently.
The toxic ones are also sure to make the managers, skip levels, and other teams know how much they contribute while playing down the contributions of others. This is one of the ways I distinguish the toxic from the merely “direct.” It’s also why it’s so important to track turnover stats, perform (and care about) exit interviews, and have processes for 360 degree and anonymous or “safe” feedback.
I’ve noticed that the aggressive and toxic are often the people who rise up the corporate ranks the fastest, with the most toxic rising the quickest. This is true in all fields. It disgusted me at first and I was really annoyed with my first few employers. Now I realize it’s pretty universal and I even sort of understand why. There is for sure a value to toe stepping sometimes, and you’ll have to do more of it the higher up the ladder you go. Also toe stepping is correlated with working hard.
How big of a problem is this in data science? I’m basing this post on roughly a dozen scenarios I’ve seen on the job, literally multiple people at each job. Some were actual problem cases; some just had different personalities or came from different cultures. If you think this post is about you, sure, take it as an opportunity to reflect. But honestly you are at most one data point and the truly toxic wouldn’t be concerned about this article.
The toe steppers I have worked with, even the ones that I think crossed some lines, really did pull teams up in the short term. There was a lack of organization. People weren’t taking initiative. Deadlines were being missed. Projects were limping towards irrelevance. Then software engineer Steve rallied the troops and suddenly the team was meeting deadlines. Steve represented the project well to management. There were real benefits.
On the other hand, I have seen situations where literally everyone on the project team was desperate to leave. The main negative effect, for the company, of toxicity is that it leads to good people leaving projects, teams, and companies. Personally, I haven’t left a job because of toxicity. But I have found myself frequently consoling despondent colleagues. Honestly, this has happened at every job I’ve had even ones where it would be a huge stretch to call the people or environment toxic. I have felt compelled to confront Steve once or twice, and to back up the testimony of new hire Nathan more times than that. I’m not going to lie to protect Steve. This is outside of my comfort zone and takes up a decent amount of energy.
So what can be done? New hire Nathan typically wants to report Steve to HR or management. My personal take on this is that it’s not a great idea unless something really bad has happened and lots of people witnessed it. Nathan will be disappointed in the results. There’s a good chance management will “take Steve’s side.” This is particularly true if Steve is a toe stepper but not all that much more. What can management really do even if there is some evidence of toxicity?
One thing I routinely see is HR or management asking Nathan and Steve to have one or multiple chats. I’m no management expert but this strikes me as a terrible idea. This is sure to stress out Nathan. It may also stress out Steve, who probably doesn’t think that he is toxic and now has appointments with someone who has already complained about them to management. Maybe it causes Steve to make small behavioral changes. From what I’ve seen, these don’t last long. They might help for the toe steppers who don’t realize when they are close to crossing boundaries. But a quick chat with management probably could have achieved the same result.
Shaking up team ceremonies or even staffing can help. Empowering new hires can help. One important point here is that managers should not only grease the squeaky wheel. Letting one or two complainers leave will just make things worse for those that stay. Reducing meetings can help.
Changing official corporate culture can help, in the long run. Screening for personality fit during the interview process can help. According to a former coworker, Stitch Fix doesn’t suffer brilliant jerks and weeds them out in the interview process. I didn’t work with any at Scoop, which emphasized corporate culture.
I mentioned 360 reviews and anonymous feedback before. Both are helpful for evaluating managers, tech leads, and team processes. Most of the organizations that I have worked at relied on “culture surveys” which include vanilla questions about job satisfaction. This isn’t what I’m talking about. The many people, like myself, who see but are not the target of slight toxicity have little reason or opportunity to remark on what they know is a potential problem. Much better to ask more specific questions about people (not just the manager!) or how interactions are structured within the team.
Sometimes organizations rely on exit surveys as well. I’ve heard many people downplay exit surveys. Disgruntled Debbie is already leaving; why would an organization make changes to satisfy her? Why would Debbie want to risk burning bridges or start a difficult conversation when she already knows she has escaped? These are good questions but in fact the people that I know who have left because of toxicity or perceived toxicity have been pretty up front about their feelings on the way out. They want the company and the people to know and to change. When I left RAND, I definitely noted what I thought was off in corporate culture in the hopes that it would be cleaned up to benefit future Kennys. I spent 6+ years at the place; I cared about it. I didn’t call out individual people, but none were truly toxic. I would bet that there is a lot of useful information that’s going unnoticed in exit interviews and attrition statistics. Companies should look at both more. The stats tell a story in and of themselves, if we’re brave enough to read it. Definitely not unique to data science.
In my experience, most of the toxicity or perceived toxicity in data science happens during informal but high profile team meetings where big shots are present and/or important business decisions are made. Steve belittles Jennifer, or her idea, so that he looks important and his idea carries the day. This is one reason why I’m a fan of document-based meetings and decision making, although I’ve rarely seen them. We are supposed to be letting the data guide us so let’s focus on the evidence.
You might be thinking that you or your project or your organization don’t have the time for any of this. Ok but do you have the time to replace the people that leave because of the toxicity or perceived toxicity?
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