Imposter Syndrome: if you can’t overcome it, use it!

By Data Science Salon

It’s no news that the tech industry is far from being gender-balanced. According to research from recruiting firm Burtch Works, not even 1 out of 5 data professionals are women. What is even more worrying is that the industry is still predominantly represented by white males while women of color are clearly underrepresented.

Thus, aiming for more diverse and inclusive teams should be a key focus of every organization. More diverse teams mean wider perspectives, better work quality, broader business opportunities, and more.

While organizations re-adjust and re-evaluate their hiring practices, there are amazing and supportive communities of women that aim to not only provide space and resources for one another, but to continually do the difficult work of making the industry more inclusive.

We had the lovely opportunity to speak with a data scientist we admire — Vidhi Chugh,  Staff Data Scientist at Walmart Global Tech (previously at Blue Yonder), and asked her about her opinion and advice on how to reach and create a more diverse industry.

DSS: What is your advice to women who aspire to a career in data science?

Vidhi: You should not get overwhelmed by the breadth of information that this field entails. Just work on the basics and pick up one particular field and take things one by one. Make sure you are evolving every day, learn something new everyday and eventually the dots will connect and begin to make sense.

DSS: According to a report by the National Center for Women & Information Technology, the amount of women in computing occupations has steadily declined since 1991. What can be done to encourage more women to pursue data science?

Vidhi: I think one of the prime reasons is that a lot of girls don't choose STEM as their career choice and in the formative years once you have not done that, it becomes difficult to switch to a different career. In particular, in data science, the core reason is how the field itself has been portrayed as very difficult to get into. However, no one expects you to start as an expert. It is like learning a language. If you start learning the alphabet, eventually you will go from beginner to intermediate to expert. In tech, once you are equipped with the basics, it is okay to put in more practice and then put your work into action. Don't get overwhelmed by how the field is portrayed and it is not that difficult to get into it.

It is also considered a very stressful job. And to a certain extent it is, because when you start you initially start a number of things and you fail at most of them. But in that journey you learn what works and what doesn't, and you evolve as a data scientist and you build an intuition. With that intuition you are able to rule out what does not work so that the next time you face a similar problem, you will be more focused and targeted and you will know what to try next. So these approaches improve your knowledge as a data scientist and go towards your ‘wisdom bank’ as I call it.  

At Blue Yonder, I host a brown bag session in which we focus on learning from our own mistakes. If you are learning from your own mistakes and others, it is better to share it in the community so that you all evolve together. That way we have a pool of knowledge, know what doesn’t work and to not waste our time in that direction.

DSS: The lack of role models for marginalised communities has a major impact on making people feel like they do – or don’t – belong in the tech space. Have you ever dealt with imposter syndrome? How did you overcome it?

Vidhi: I have personally experienced imposter syndrome but I believe in the power of mentorship. I had the fortune of working with veteran AI/ML folks in the industry who have shaped the way I approach a problem and the intuition I have developed overtime. But only recently have I had the fortune of working with women in the industry and I can say that the experience is unparalleled. You are more comfortable sharing your concerns within the community you are working in. I feel more comfortable talking about my concerns with women in the workplace. This is primarily because you know they have been there and they understand your concerns better. They are like a guiding light and they give you a push that “I can do well.”

Regarding your question on imposter syndrome, I believe it is everywhere regardless of gender, which group you belong to, how many years of experience you carry or the kinds of brands you have worked with. One way I have overcome imposter syndrome is by not having a myopic view of the situation. You have to come out of that short sighted assumption that you want quick results and if it doesn't work you don't know what is going to happen next.

So, if you think that you are having imposter syndrome, it is basically because you know that somebody knows something that you don't know. And I think that’s the first step where you should congratulate yourself because you have acknowledged what you don't know. The next step is to just work on it. The more imposter syndrome hits you, the better it is to acknowledge that, work on it, and then the number of times you repeat this process, you will become a better data scientist. Take imposter syndrome as something positive and don’t feel bogged down by this syndrome. It is everywhere.

DSS: You are an advocate for Responsible AI. Where do you see the biggest challenges when it comes to using AI responsibly and how can these be approached?

Vidhi: I think AI as a technology itself cannot be blamed as being irresponsible. It is the person or the group who is putting it to good use or not. So there is a lot of good brain power out there that can develop sophisticated and advanced solutions. The whole difference is how they are put to use. Intent is something that matters a lot in AI. So, there are two things that come to mind.

First, people that are working on the ground and closer to the data—data analysts, project managers—they know what can go wrong in this situation. So whenever you sense anything doubtful, or you can sense that it won't be put into good use, always talk to the leaders within the team. This brings me to the second point: if you want to talk it out, you have to have a matrix or level of people you need to approach. You should be aware of who to approach. 

It is always good to give the benefit of the doubt to humanity. It is very difficult for a mal-intent to skip through so many pairs of eyes and make its way to the application.

DSS: At DSS Elevate you’ll be talking about data science in banking solutions. Can you give us a brief idea of what the attendees can learn from your talk?

Vidhi: I think there are a lot of domains where the interpretability of a solution is very important. Banking being one of them, so you want to take actions based on the predictions they are giving you. The talk is going to explain one of the ML algorithms that lets you do just that and not to just that extent. It helps you gather some insights about the data and you can see the data from multiple perspectives. That's the beauty of the algorithm. I look forward to joining the conversation and I hope you’ll learn a lot from it!

DSS Elevate is an initiative by Data Science Salon to elevate and connect the voices of women in data science and encourage companies to set better standards in hiring women for data-intense roles. To expand our mission, we are committed to creating the most inclusive and diverse community for women including BIPOC, members of the LGBTQIA+, and other underrepresented groups. By elevating our community, we have the opportunity to build a truly incredible and thriving field that influences decision-making at every level in a meaningful way. 

Get the latest data science news and resources every Friday right to your inbox!