Better Allies

During my 7+ years in tech, I’ve encountered sexism, but I’ve also had the good fortune to work with some really great guys. These guys cared about me and my work, and lots of the small things they did to help me (sometimes without realizing they were doing anything!) added up to big things for my career over time.

In hopes that others can follow their example, I’d like to share some of the things that “good guys in tech” have done to help my career:

Sharing positive feedback (with my boss)
Over the years, some of my consulting clients made an effort to praise my work *in front of my boss*, which directly helped me to get promoted.

Each promotion has meant more experience, more skills, and higher compensation — in addition to helping to set me up for the next step(s) in my career.

Recommending me
I got my last job because a guy on my future team recommended that his boss take a look at my resume. My resume got screened out through their recruiting process, so I know that I literally would not have gotten the interview if it weren’t for his recommendation. It’s hard to understate how important that recommendation was for my career.

Similarly, many former coworkers and clients have also written formal recommendations for me on LinkedIn, which helps to build trust in my work and establish credibility beyond a single job or boss.

Sharing salary information
I had a male coworker who started in the same position as me, was promoted to a manager role, then switched to a new company. Before he left, he shared his salary in my position, after being promoted, and at his new company.

This helped me to navigate salary negotiations (both internally and externally), ensure that I was being paid fairly, and to evaluate new job offers. Knowledge is power when it comes to salaries and a little bit of “extra” information can go along way.

Sponsoring me
My last company had a highly selective quarterly awards program. My boss put time and effort into my nomination, then went to his boss to ask that he throw his weight behind the nomination as well to give me a better shot at getting the award. I got the award, and I’m positive that having the extra backing had an impact.

This is the difference between mentorship and sponsorship — a mentor might help you gain the skills to earn an award, but a sponsor will nominate you, then go to bat to personally advocate for you. A sponsor has skin in the game. Women are over-mentored and under-sponsored, and we need people with social and political capital to promote us and help us to advance.

Asking about parental leave policies publicly (and lobbying for better ones!)
During an HR / benefits session, a male coworker asked about parental leave so that I wouldn’t have to. It can be quite awkward to have people assume that you’re pregnant (or soon to be) if you’re talking about parental leave policies, and this saved me from having those uncomfortable conversations.

The same guy also wrote a letter to the CEO citing his experiences and how a flexible schedule helped him and his family during pregnancy and beyond. Parental leave helps everyone!

Promoting my work (even Twitter helps!)
When someone references things I’ve written or retweets the things I’m working on, it helps to amplify my message and build my network.

This also provides potential for new opportunities and conversations with people I may not have reached otherwise. (Plus, it never hurts to have people supporting you and talking about your work!)

Supporting women-focused groups
Before launching R-Ladies Austin, we had several men reach out to see how they could help us grow. They offered time, training materials, books for raffles, advice, meeting places, sponsorships, speaking opportunities, and more. This helped a lot as we were getting established.

Similarly, our local Austin R User Group goes out of their way to promote R-Ladies events without me even asking. This helps us to expand, reach new members, and makes us feel supported and welcome in the tech community.

Empathizing (and humor doesn’t hurt!)
I’ve been lucky to have male coworkers who at least try to “get it” when it comes to gender in tech, and who have had my back during tough moments. Some of my favorite coworkers have made laugh out loud after being frustrated by something casually sexist that a client said. That stuff is the worst, and a little bit of empathy and humor can go a long way.

Working to improve gender ratio at tech events
Quarterly, our R-Ladies group teams up with the larger user group for a joint meetup, and recently we asked for volunteers to give lightning talks and ended up with more speakers than slots (a great problem to have for an organizer).

I was prepared to step down to give my slot to another woman in our group. Instead, the male organizer chose to give up his slot so that the event would have a higher women-to-men gender balance. To me, this small action speaks volumes about the type of inclusive tech community we’re working to build here in Austin.

Being a 50/50 partner at home
My husband has picked up domestic “slack” while I study, organize meetups, attend workshops, and travel to conferences (among other personal pursuits). I’m all about being a hashtag-independent-woman, but the dogs still need to go out even if I’m doing back to back events after work.

The balance of responsibilities shifts from week to week, but in the end, we’re partners and teamwork is what makes it all work. The fact that my husband supports my career and is happy to help out at home makes more things possible.

Asking what men can do (better)
A former boss tries his hardest to promote women in tech, and one of the things he’s done best is ask for specific ways that he can be most helpful. This has lead to lots of productive conversations around things like hiring and mentoring.

The act of asking also lets me know that he’s open to feedback and questions. This list started in large part because he’s constantly asking for concrete, actionable ways that he can help women in tech, and I appreciate that.


But wait, there’s more!
All of the above are things that good guys have done for me personally. I asked my network on Twitter whether they had any personal experiences and got lots of feedback. Here are more great (concrete and actionable) ways that men in tech have helped women in my personal network:

  • Helping brainstorm talk proposals (via Stephanie)
  • Offering training to help women build their skillsets (via Susan)
  • Literally just saying “well done”, “that was great”, “I’m impressed”, etc. (via Alexis)
  • Fixing a salary discrepancy after investigating and realizing a woman is underpaid (via Angela)
  • Letting women know that you have confidence in them (via Mara)
  • Supporting women while they are out on maternity leave (via Elana)
  • Asking point-blank, “What is holding you back?” and helping (via Alison)

One more thing
Ladies, if there’s a concrete action that someone has taken to help your career, I’d love to hear about it in the comments or on Twitter [I’ll update this post with any new suggestions].

Also, it’s worth mentioning that none of the above actions are gender specific — plenty of women have helped my career as well (in similar and different ways) — or specific to tech. Anyone can make a big difference on someone’s career — these are just a few ways that some good guys  have helped mine, and I hope they help illustrate small, concrete ways that we can all be better allies to one another.


A Data Science Tour of Duty

In September 2015, I was looking for a job in Austin and started interviewing at Yodle, a marketing tech company. Yodle was looking for someone with experience in data and predictive analytics, and I was looking for a company where I could learn how to code and work on new, interesting problems. It was a good match, but one thing that made this opportunity stand out was the way that my soon-to-be boss described what my time there would be like — a “tour of duty”. Tim was building a data science-y team and was testing out a management framework he had discovered called “The Alliance”. I was intrigued.

A tour of duty?

I’ll pause here for a quick explanation of The Alliance and the tour of duty concept.

The Alliance was created by LinkedIn cofounder Reid Hoffman to address a lack of trust and alignment between employers and employees in a networked age. The gist is that since pensions aren’t really a thing in tech and most people no longer spend a lifetime at the same company, employees behave more like free agents — they are willing to leave a position when the next good thing comes along without concerns about loyalty to their employer.

The Alliance outlines a new employer-employee compact where employers can retain employees better by being open and honest about this situation and focusing on how they can add mutual value to each other. One way to do this is to establish a tour of duty for each employee — a commitment by both parties to a specific and mutually beneficial mission (with explicit terms) to be accomplished over a realistic period of time. For a more thorough explanation, check out the visual summary below.

There are some other key components of The Alliance beyond the tour of duty that I’m not going to outline here. The framework for having open and honest conversations about career goals and timelines was also interesting and impactful for me, and worth a read if you’re interested.

My tour of duty

My transformational tour of duty started with making some goals to be incorporated into a formal growth plan. (One crucial piece of The Alliance is that the mutual agreement between employers and employees should be written down, which includes the things that each party hopes to achieve during the tour of duty.) My initial goals included learning to code, doing innovative data analysis, and learning to automate things, all of which would be put to use on a project to build in some automation around our A/B testing.

Building trust incrementally is another facet of The Alliance — the relationship deepens as each side proves itself. I wanted to learn how to code, and Tim gave me about two months of paid development time to ramp up on company practices and learn to code before diving into analyses. This built trust for me immediately, and because my boss was willing to invest in me, I was happy to invest in his mission and doing great work for our team.

The timeline we set for this first tour was about two years, and especially at the end of my ramp-up period, I was feeling really good about it. We did weekly 1:1’s to check in, and I was able to freely talk about how my goals were changing as we accomplished things along the way.

…Okay, multiple tours of duty

Four months after I started, Yodle was acquired by If you ever want to throw a wrench into long-term job plans, an acquisition really is a great way to go. Due to several shakeups that were beyond my boss’s control, I actually ended up completing three distinct tours of duty — one in marketing analytics and automation, one in product analytics (including feature research and user behavior) and a final tour in production machine learning and data science.

During these times, Tim let me know when he was having doubts about projects or when tectonic shifts in our organization’s structure were coming. His openness and honesty empowered me to be open and honest. At one point, I told him that I didn’t want to do product analytics work — my job at the time — anymore. (Note: I actually like product analytics, but I really wanted to learn how to build machine learning models and put them into production.) My goals had grown with my skillset, and he added me to a team where I could pick up these new skills while continuing to add value to the business.

Multiple shorter tours was definitely was not what we initially planned, but we were able to be agile and adjust as needed, and I’m grateful to have gained valuable experience in multiple arenas. My last tour in particular was exactly the kind of transformational launchpad that we talked about when I first joined.

The end of the road

Tim had always been very up-front that if my dream job came along, I should take it. In turn, he let me know when he was contacted by dream-job-level prospects, and kept me up-to-date on how he was feeling about his role, his missions, and his career path. When I had an inkling of when it would be time for me to move on, I told him.

Another goal of The Alliance is to extend the relationship between employer and employee to be a lifetime relationship that exists beyond the scope of a single job. I feel really good about this. I’d love to work with Tim again because I know he cares about my career beyond a single job, and he demonstrated this by giving me the opportunity to work on projects that would grow my skillset and enable me to move on to the next thing.

Post-tour thoughts

If I had the chance to work within The Alliance framework again, I’d take it.

The Alliance is rewarding, but it’s also tough. It takes commitment from both parties and a lot of gradual trust to get the point where you can talk openly about your career beyond the scope of a single job (but once you get there, it’s worth it). When plans changed or gave way to new ones, being able to talk openly and honestly about what was and wasn’t working allowed me to build my skills while helping the company with its goals — a win-win.

My tour of duty was a transformative step in my career, exactly as it was designed to be. With a roadmap, a reasonable amount of time to dedicate, and a clear explanation of how my projects would be mutually beneficial to me and the company, I was enthusiastic about my work. I was given the opportunities I needed to learn new skills, build cool things, and work with great people. As my time at comes to an end, I’m happy, well-equipped, and ready to start my next tour of duty.

If you’ve worked under The Alliance framework, I’d love to hear about your experience and whether there’s anything you might add or change — feel free to ping me on Twitter.

One Year of R-Ladies Austin 🎉

Today marks one year of R-Ladies Austin! The Austin chapter started when Victoria Valencia and I emailed R-Ladies global (on the same day!) to ask about starting a local chapter. It was meant to be — the ladies from R-Ladies global introduced us, and the rest is history.

For anyone who hasn’t heard of R-Ladies, we are a global organization whose mission is to promote gender diversity in the R community by encouraging, inspiring, and empowering underrepresented minorities. We are doing this by building a collaborative global network of R leaders, mentors, learners, and developers to facilitate individual and collective progress worldwide. There are over 60 R-Ladies chapters around the world and we continue to grow!

Here in Austin, it’s been a busy year. So far, we’ve hosted 16 meetups — including seven workshops, two book club meetings, two rounds of lightning talks, a handful of happy hours, a movie night, and a visit from NASA.


To celebrate our first R-Ladies anniversary, I thought it would be fun to answer some questions with Victoria around our journey so far:

What has been the best part of working with R-Ladies?

Victoria: The best part has been connecting with women in our community that share similar passions and interest in data! It has been so fun. Also, the R Ladies hex stickers are pretty awesome. 🙂

Caitlin: It’s been great to be a part of such a supportive community and to meet so many brilliant women, both here in Austin and in other cities. Since joining R-Ladies, I’ve built a great network, learned cool things, and had a lot of fun along the way.

Have you had a favorite meetup so far?

Victoria: My favorite meetup by far was our book club for Dear Data by Giorgia Lupi and Stefanie Posavec. We started by discussing the book and the types of visualizations and data Giorgia and Stefanie shared with each other. Some were funny and some were sad but all of them were inspiring! We followed by creating our own visualizations in a postcard format of the beer list at Thunderbird Coffee. Who knew a beer list could be so fun to visualize and that each of us would think to do it in such different ways! It was a blast.

Caitlin: I love the book club meetups too — it’s a great space because we can do anything from have deep discussions on the ethical impacts of algorithms in society (I’m looking at you, Weapons of Math Destruction) to getting really creative and using colored pencils to dream up artistic ways of visualizing data. I also loved having David Meza come down from NASA in Houston to talk about knowledge architecture. It would be an understatement to say that he’s been supportive since day one, because he actually reached out to us long before our first meeting. (I guess “supportive since day -75” doesn’t have quite the same ring to it, but it’s true.)

What’s the biggest thing you’ve learned after one year of organizing R-Ladies?

Victoria: That managing a meetup is a fair amount of work, but certainly worth the effort! I have also learned that the R Ladies community is strong and close knit and super supportive! It has been great connecting and learning from them.

Caitlin: I agree with Victoria’s take — managing is a lot of work but also *very* worth it. I’ve learned a lot about building community through collaboration. Working with other local meetups has helped us to expand our reach and provide more opportunities for the women in our group. It’s also been very cool to learn more about the tech community on Austin. We’ve been fortunate to receive lots of support from local companies and other tech groups, and it’s been nice to get more plugged in that way while building a distinct community that adds something new to the mix.

How has R-Ladies helped you (personally or professionally)?

Victoria: R-Ladies has helped me by allowing myself time to learn about cool R stuff I did not know before! It has helped me to learn more efficient ways of coding by going through all of the chapters of R For Data Science, how to relax with colored pencils, data, and beer, and that opened my mind to different perspectives from fellow R-ladies about the continually evolving and expanding world of data that surrounds us.

Caitlin: I can’t say enough good things about the R-Ladies community. The individual chapters help to build local communities and strong networks of highly-skilled women, and the global chapter works hard to promote the work of R-Ladies to the larger global community, including people who might not see that work otherwise. Especially since a lot of women are one of few women on their team (or the only woman on their team), it’s great to have a network who can relate and provide feedback and advice (on all sorts of things) when you need it. On a personal level, I’ve built relationships with amazing women (both in real life and virtually) through R-Ladies, and it’s opened up some opportunities that would have taken a lot longer to find on my own.


The next 12 months

We’ve grown a lot this first year (we’re over 275-strong!), and we’re hoping to grow even more in the next 12 months. If you’re in Austin and haven’t made it out to a meetup yet, we’d love to meet you! We’re beginner friendly, positive, and dedicated to promoting gender diversity in the R Community (and tech in Austin more generally). And even if you are just interested in data and maybe learning more about R we want you to join us as well!

If you’re not in Austin, but want to support R-Ladies, I’d encourage you to check out R-Ladies directory the next time you’re looking for speakers or for local women to reach out to — there are lots of women out there doing amazing things, and R-Ladies is making it easier and easier to find and connect with them.

The two biggest things that we’ll need in the next 12 months are speakers and space. If you use R and have learned a cool thing, discovered a neat package, done an interesting analysis, or have anything else you want to share, we’d love to hear from you. And if you have space available, we’re always looking for new spaces to host the various types of meetups we put on. Please get in touch with us; we’d love to hear from you!

Thanks for a fantastic year, and looking forward to the next 12 months!

Caitlin and Victoria

Only 60% Sure

I have a fantastic coworker who I’ve been pair programming with a lot with lately, and he does one thing that I wish everyone did:

He has a habit of stating something (usually an answer to a question I’ve asked), and then after a beat, saying something like, “I said that very confidently, but I’m only about 60% sure“. This is usually followed by a suggestion to firm up his answer, like “you should ask X person”, “you should try it and see what you think”, or “you should maybe research that more”.

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Here’s why I love that follow-up response so much:

  1. This answer builds my trust in him (because I know he’ll admit if he doesn’t know something), and builds my confidence in his answers overall. On the flip side, if he makes a statement and doesn’t qualify it with some level of uncertainty, I trust it as-is and don’t feel like I need to research it or double-check afterwards.
  2. This models great behavior around questions for our org as a whole by making “I’m not sure” an acceptable way to answer to a question. By including additional suggestions of ways that I can get answers in his response, he’s setting me up to find the best possible answer, which is better and more efficient than getting an incomplete answer from someone who might not be the best person to cover a given subject area.
  3. This encourages more questions. I’m not afraid to ask tough or opinion-based questions because I know I’ll get a thoughtful and balanced answer. Asking more questions has led to a deeper understanding of the technologies and products I’m working with — a win-win for him, for me, and for the company.

Since I’ve heard him say this, I’ve started incorporating it into my own conversations, both professional and personal. It’s a small thing, but it makes a big difference in the way we interact, and I would love to see more people adopt this habit.

Imposter Syndrome in Data Science

Lately I’ve been hearing and reading lots about imposter syndrome, and I wanted to share a few thoughts on why imposter syndrome is so prevalent in data science, how I deal with it personally, and ways we can encourage people who are feeling the impact.

Why is imposter syndrome so prevalent in data science?

Data science has a few characteristics which make it a fertile ground for imposter syndrome:

  • Data science is a new field.

    DJ Patil and Jeff Hammerbacher were the first titled “data scientists” only about 7(!) years ago (around 2011). Since then, as we’ve all been figuring out what data science *is*, differing definitions of “data scientist” have led to some confusion around what a data scientist should be (or know). Also, because “data science” wasn’t taught in colleges (as such) before then, the vast majority of data scientists do not have a diploma that says “data science”. So, most data scientists come from other fields.

  • Data science is a combination of other fields.

    Depending on who you ask, a data scientist is some combination of an analyst / statistician / engineer / machine learning pro / visualizer / database specialist / business expert. Each of these are deep positions in their own right, and it’s perfectly reasonable to expect that a person who comes to data science from any one of these fields will have significant gaps when it comes to the other fields on the list.

  • Data science is constantly expanding with new technologies.

    As computer memory becomes cheaper, open-source becomes more popular, and more people become interested in learning and contributing to data science and data-science-adjacent fields, the technology surrounding data science grows at a very healthy rate. This is fantastic for the community and for efficiency, but leads to lots of new technologies for data scientists to learn and a culture where there is pressure to stay “on top” of the field.

So, we have people from a variety of backgrounds coming to a new field with many applications  whose boundaries aren’t clearly defined (thus causing inevitable gaps in their knowledge of that field as a whole), and where technology is changing faster than a single person can keep up with. That is the plight of a data scientist in 2018, and why so many people feel the effects of imposter syndrome.

My Secret for Dealing with Imposter Syndrome

Every single data scientist that I know (and you know) is learning on the job. It might be small stuff (like cool tools or keyboard shortcuts) or bigger stuff (like new algorithms or programming languages), but we’re all learning as we go, and I think it’s crucial that we acknowledge that. For me, it’s simultaneously really exciting to be in a field where everyone is learning, and also kind of intimidating (because what if the stuff I’m learning is stuff that everyone else already knows?), and that intimidation is a form of imposter syndrome.

The way that I’ve dealt with imposter syndrome is this: I’ve accepted that I will never be able to learn everything there is to know in data science — I will never know every algorithm, every technology, every cool package, or even every language — and that’s okay. The great thing about being in such a diverse field is that nobody will know all of these things (and that’s okay too!).

I also know that I know things that others don’t. I’ve built predictive models for dozens of colleges and non-profits, have experience on what it takes to create and analyze successful (and unsuccessful!) A/B tests, and am currently learning how to do machine learning models in production. These are not skills that everyone has — there are people who know more about computer science than I do, or machine learning, or Macbook shortcuts — and that’s okay. Diversity is a good thing, and I can learn from those people. There’s a great Venn diagram which illustrates the relationship between what you know and what other people know, and how they overlap. What you know is rarely a subset of what other people know; your knowledge overlaps with others and also sets you apart from others.

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Image Source

Community-wide Techniques for Reducing Imposter Syndrome

If we can agree that all data scientists are learning on the job, I think the best things that we can do for reducing imposter syndrome in the larger data science community are to be open in acknowledging it and to work towards fostering a healthy learning environment.

  • Get comfortable with “I don’t know”

    I love when people say “I don’t know”. It takes courage to admit when you don’t know something (especially in public) and I have a great deal of respect for people who do this. One way that we can make people more comfortable with not knowing things is to adopt good social rules (like no feigning surprise when someone doesn’t know a thing, and embrace them as one of today’s lucky 10,000 instead).

  • Don’t “fake it ‘til you make it”

    Sure, it’s good to be confident, but the actual definition of an imposter is someone who deceives, and I think we can do better than “faking it” on our way to becoming better data scientists. “Faking it” is stressful, and if you get caught in a lie, can potentially cause long-term damage and loss of trust.

  • Encourage questions

    The benefit to asking questions is two-fold:1) You gain knowledge through conversation around questions
    2) Asking questions publicly encourages others to ask questions too

    Asking questions is exactly the kind of thing data scientist should be doing, and we should work to encourage it.

  • Share what you’re learning

    When I see others share what they’re learning about, it helps me put my own learning in perspective — and whether I know much about the topic or not, it’s encouraging to see other people (especially more experienced people) talk about things that are new to them.I’ve started a personal initiative to track the things I’m learning each week on Twitter using the hashtag #DSlearnings. Feel free to have a look at the archives (I’d love to chat if you’re learning similar things!), and to add your own learnings to the hashtag.

A little bit of transparency goes a long way towards staving off imposter syndrome. We can embrace both being knowledgeable and not knowing things — and do so in public.

I’d love to hear ways that others deal with imposter syndrome, and about things you’re learning (feel free to use #DSlearnings or make your own hashtag!) along the way.

PS: Thank you to @jennybryan and @dataandme for the Venn diagram!