Supporting Women in Data ScienceReading time: 6 minutes
At 22, I joined Rapid Insight as the company’s first statistical analyst. As my first job out of college, helping others to build predictive models was a bit nerve-wracking, but my boss was very supportive and I was able to build my modeling skills by training directly with the CEO, which was a rare and valuable opportunity.
I quickly learned the skills I needed and loved being able to help customers to build effective predictive models. The experiences I gained led to promotions and allowed me to grow my skill set significantly. After about four years, I left NH (and Rapid Insight) to move to Austin, TX, but continued to work on data and analytics — my current role is a Sr. Data Scientist at Shelfbucks, and IoT startup focused on the retail space.
Recently, Paul Kirsch reached out to let me know that Rapid Insight is putting on a “Women in Data Science” series, and asked me to write an introduction. During my time at Rapid Insight, I worked with, supported, and learned from plenty of amazing women from varied backgrounds who were making large impacts with their individual data. I’m happy to do that again today and encourage you to check out their webinars to learn about some of the cool things that women in data are building.
While I was at Rapid Insight, my male coworkers and clients were very supportive and helped to get me to where I am today. They cared about me and my work, and lots of the small things they did to help me (sometimes without realizing it!) added up to big things for my career over time. As a way of celebrating and supporting the women in the current “Women in Data Science” series, I wanted to share a few ways that some of the “good guys in tech” have had an impact on my career in the hopes that others can follow their example.
If you’ve been looking for concrete and actionable ways to be a better ally, I hope these help:
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.
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.
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 the 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 in data science 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 co-workers 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 data science tech, and one of the things he’s done best is asking 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.
It’s worth mentioning that none of the above actions is 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.
Check out the upcoming “Women in Data Science” webinar series (links below), and let us know if you have other ideas on how we all can help and support one another:
Michelle will review how she utilizes Veera Construct to prepare her data and automatically update the Student Learning Data System, a Microsoft SQL Server, for use in 90+ Tableau Dashboards.
Data Analyst, BI Specialist
University of Cincinnati
Modeling Major Gift Likelihood
Translating Data Into Action
Abigail will discuss how her Advancement team translated their predictive model for major gift likelihood into descriptive, easy-to-interpret scores that intrigued colleagues to use them before the project was fully rolled out.
Assoc. Dir. of Advancement Analytics
“One size fits all” doesn’t always work for predictive models. Sarah will be shares the approach she took to develop multiple freshman retention models targeted at unique segments of the University of New Haven’s student population.
Sarah Caro, PhD
Senior Research Analyst
University of New Haven
The Journey: Development Records to Business Systems at The Whitney Museum of American Art
Bridget shares the evolution from an initial focus on fundraising data for the build-out of infrastructure to support a broader institutional constituent strategy. The Business Team is guided by three core data principles: Collection, Accessibility and Governance.
Director, Business Systems
The Whitney Museum of American Art
Note: Excerpts of this post originally appeared on Caitlin Hudon’s blog.