How to Break Down Silos and Find Community as a Data Scientist



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How did you decide to go into data science and — more specifically — into the area of data science you’re currently focused on?

My journey into data science has been a pretty interesting one. I am an electrical engineer; post-graduation, I worked in a power utility company in the capital city of India. My daily work revolved around power transformers, grids, and substations. However, as luck would have it, I got a chance to work in a department of electronic meters called Advanced Metering Infrastructure (AMI), where we would sift through the humongous electronic-meter data to identify potential fraud or fault in meters. This was a life-changing moment for me, as I started seeing the immense value that data could bring to any business. I started researching and literally opened a pandora’s box, albeit in a positive way.

Back then, I wasn’t aware of a field called data science. I wondered if I could fit in or if a person with my experience could bring value to this field. Around that time I went on a maternity leave, and that, I guess, sealed my fate. It gave me time to ponder over my future. Eventually, I took a leap of faith and resigned from my job. Here I was with a little munchkin, no job, no sleep, and lots to learn. Going back to the basics and studying everything from scratch was both challenging and exciting at the same time. I had a background in programming and math, so that was a bonus. After phases of learning, unlearning, failures, successes, and rejections, I changed the trajectory of my professional life.

I currently work at H2O.ai, which is an automated machine learning company. Our goal is to democratize machine learning by making its power available to everybody, rather than a select few.

Looking back at that transition, what kinds of challenges or obstacles did you have to face?

There were plenty, which I believe is very common for people who transition from a non-CS background into data science. The biggest issue is breaking into the field. Companies want candidates with some machine learning experience, and, unfortunately, we don’t have any. No matter how good our resume is or how much effort we are ready to put in the job, we just don’t make the cut. This is both demoralizing and dangerous. On the pretext of giving experience, many companies exploit candidates and ask them to work for free. Such malpractices are widespread in this field, and I, too, have fallen victim to them.

After having gone through similar painful experiences, I stopped applying for any roles. On the contrary, I decided to focus my efforts on my abilities rather than my shortcomings. I came out of silos and started engaging with the community. I realized others like me were sailing in the same boat. These engagements slowly turned into meetups and webinars. I started engaging with people on platforms like LinkedIn and Twitter. And finally, writing was the missing component. It gave me voice, visibility, and the confidence to create a name for myself in the field of data science.

What do you enjoy the most about your current role? What projects do you tend to gravitate towards these days?

As a Data Science Evangelist at H2O.ai, my role typically lies at the intersection of data science and community. While, on the one hand, I work on some of the latest technologies in the space of artificial intelligence, I also get to interact with the community. I love to create awareness about data science in general and H2O.ai’s products in particular. As Guy Kawasaki puts it, “Evangelism isn’t a job title; it’s a way of life.”

My work requires a lot of self-awareness and willingness to stretch and grow in the role. I consider myself fortunate to work in this field and touch many lives through my work.

Apart from this, I’m also involved in building communities for women and underrepresented folks. I have gone through the same challenges as them, and I want to apply my learnings to help them. As part of WiCDS, we organized a Blogathon a few months back with the sole objective of encouraging people to write.

We also organized mentorship sessions along with the Government agencies to help mentor undergraduate female students. We encouraged them to create projects along with their mentors so that they understand the importance of ideation, innovation, creation, and thinking.

Apart from this, we also organize webinars regularly so that the community gets to know what is happening in the industry. Additionally, this also provides a platform for speakers to hone their public speaking skills.

We had to take a step back due to the pandemic situation in India, but things are settling here, and we will relaunch some of the other initiatives.

You mentioned public writing earlier—what was it that inspired you to start, and how does it fit in the context of your other professional activities?

Honestly speaking, I never thought I would be writing publicly. Why would somebody read my articles when there are already exceptional writers? These thoughts deterred me from writing for quite some time. Forget about writing; I was wary of even posting things publicly on social media. Then, in 2018, I posted my first tweet tagging Andrew Ng, and to my utter surprise, not only did he retweet it, but he also featured it in his newsletter. This was fun, and soon my follower count started increasing. This was a big lesson for me. I realized how important it is to put your work out in public.

I then experimented with my articles. I published a few blog posts on Medium, but the reception wasn’t great. Feedback is the breakfast of champions, and as for me, I had none. Then slowly and gradually, things started looking better. Publications on Medium started approaching me to submit my articles. Publishing via publications suddenly increases your visitor count exponentially. Soon after, I started getting suggestions from people on what I should write next or which topics I should cover. This set things in motion, and my life as a writer took off. The beauty of writing is that it is very fulfilling and rewarding. Even today, when I finish an article and hit the publish button, it brings in a pleasant sense of accomplishment.

Writing is also an integral part of what I do at H2O.ai. In fact, we are all highly encouraged to write as we believe in the philosophy of “content is king.” We have software updates happening frequently, new product launches, and new solutions, and it is through blogs and writings that we inform the public. Apart from the technical articles that I write for H2O, I also run a Kaggle Grandmaster’s series where I present the stories of established data scientists and Kaggle Grandmasters at H2O.ai, who share their journey, inspirations, and accomplishments.

It is especially overwhelming when people reach out to me based on a piece that I have authored. I have connected with so many people from different nationalities just through the medium of my writing.

Looking into the future, what kind of change do you hope to see within the data science and AI community?

As opposed to a few years back, today many people from diverse fields and backgrounds are making their foray into data science. This is a positive step in the making.

Data Science is a diverse field. As a result, it makes all the more sense to bring together people from different genders, backgrounds, and ethnicities. This way, we can bring in more creativity and allow knowledge, discoveries, and innovation to flourish. It will require a collaborative effort from society to make diversity and inclusion a vital part of the ecosystem.

Apart from this, I would like to see more “data science for good” initiatives from the corporate world. Data has a lot of power, and a large section of society could benefit from them. The pandemic has taught us that wealth inequality is growing, and it is affecting us all. Therefore, as a society, we should come forward and use our skills and technology to help those in need. Data science should be used for social good, and it is vital to look beyond profits and think about society as a whole.

AI/ML

Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot



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