Q&A – Reshama Shaikh, Data Scientist

At D/SRUPTION, we speak to and collaborate with innovators across multiple different roles and industries

We ask change leaders from our expert network to give their insights on what they see as the biggest hurdles, considerations, and solutions. What do they really see as the central challenges, how are they handling change, and where do the real opportunities lie? 

We spoke to data scientist Reshama Shaikh about the development of diverse data communities and the expansion of data science. 

Who are you and what do you do?

I am a independent data scientist/statistician with skills in Python, R and SAS. I worked for over 10 years as a biostatistician in the pharmaceutical industry. I am an organiser of the Meetup groups NYC Women in Machine Learning & Data Science and PyLadies.   I also write and teach topics in data science.  

What is the most exciting thing about your field of work?

Technological advances touch us all in areas of our lives. There is a massive availability of data as a result of this technology. In turn, an entire ecosystem is necessary to understand and maximise the information from this data as well as its sphere of impact, which includes its producers and consumers.  

The most exciting thing about my field of work is that the terms ‘data science’ and ‘AI’ have entered the English vernacular. We are living in the era of the Fourth Industrial Revolution, and all the potential that comes with that. Statistics, data and programming was quite an insular field a couple of decades ago. Today, people from across a variety of fields are understanding and using the language. Individuals from all backgrounds are excited to learn and participate, and the community has phenomenal opportunities to influence that, including public policy, education, healthcare and so much more.  

What do you think are the biggest opportunities in your field today?

Data, technology and artificial intelligence can have significant impact in the law, government, and healthcare.

What communities, materials, or experiences have you found particularly inspirational?  

I am inspired by a variety of communities and people, both within the data world and other fields. NumFOCUS, which supports open source software has been doing impressive work to increase diversity in scientific computing.  I attend tech meetups, listen to podcasts and take online courses. My favourite podcasts include the Recode Decode podcast, Dataframed, and Psychology in Seattle.

What particular companies do you admire, and why?

Google is an obvious choice. What many people may not know about Google is that they have a programme called ‘Women Techmakers’, and that they have an entire team to support this initiative. They provide resources and events for women in community leadership programmes, with an overall expansive goal of increasing and supporting women in tech. They support women at the grassroots level to increase women and diversity in tech. Fast.ai is also one of my favourites. Their goal is to make deep learning accessible and easier to use. Their online course is free, their community is inclusive, and folks from all around the world participate from every background.  

Can you share any significant failures which have helped your path to success?

In the late 1990s I left a full time job which I absolutely loved, to attend graduate school to pursue a PhD in statistics. After two full years of study, I discovered it really was not for me, and it was a major disappointment and a set back in my career. It took several years to get back into the field at a reasonable level, and I begrudgingly told myself it wasn’t a waste. Today, I know with confidence it was not for naught.

During my time in graduate school, I taught for two years, which has been immensely helpful to me, both in terms of communication, public speaking, teaching and writing documentation. I met wonderful people there with whom I am still close friends. And the statistical theory and programming in R that I learned during that time has been especially helpful for me in data science and deep learning today.

I used to have a long list of failures. Then, I learned that ‘failure is an opportunity to learn and grow’. I rephrase failure as ‘situations that did not turn out the way I expected’.  

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