A chat with Sameera Inapakutika: Resident data scientist at leading tech startups

A chat with Sameera Inapakutika: Resident data scientist at leading tech startups
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Sameera Inapakutika is a data scientist who is responsible for significant increases in revenues and user growth at top Silicon Valley companies. Her work involving Instagram ads, search and explore; Netflix's movie recommendations and video quality; and Quora's home feed and search has earned hundreds of millions of dollars for her employers’ companies. Sameera’s work in the field of Data Science is sophisticated and wide-ranging. She has implemented cutting-edge predictive algorithms, created data infrastructure from the ground up, and worked closely with C-level executives on business strategy and company-wide initiatives.

Sameera believes in giving back to the community. She regularly mentors data scientists and entrepreneurs. She is a founder of the TiE50 (TiE is the world’s largest entrepreneur network) startup competition, and has judged several startups over the years. Sameera was a force in Stanford’s entrepreneurship community, as the Vice President at BASES. She led the popular E-Challenge business plan competition.

Sameera has repeatedly demonstrated an unwavering ability to apply advanced scientific and business acumen into large profits for various different companies. We spoke with her to learn more about her journey, and get her advice for aspiring women and data scientists.

Sameera

How did you get into Data Science? I studied Electrical Engineering throughout my academic career—first at BITS Pilani in India, then at Stanford University in California. My field of study involved building a strong foundation in linear algebra, signal processing, statistics and mathematics. These are the disciplines that power present-day data science and machine learning. So it was natural to get into this profession where I could apply what I'd learned to the growing Internet industry, as well as gain valuable experience in the process.

You went to colleges in India and the US, followed by key roles at successful companies in the Silicon Valley. That sounds like a dream career.

Actually, it's been immense amount of hard work. As a woman and then as an immigrant, you're at a disadvantage in terms of knowledge and confidence. Engineering is a male-dominated field. Silicon Valley is an aggressive, fast-moving place, where you absolutely need to have high confidence to survive. Growing up in India, I got much less freedom and exposure to the world than my male counterparts. For several reasons: women were (and are) seen as a dependent or subservient group and majority of girls did not focus on career. There was pervasive gender-segregation and a female-unfriendly atmosphere at most places. I've had to fight direct biases growing up, and indirect biases every now and then at work. Working in the Silicon Valley also means a trade-off between short-term success and long-term learning and well-being. It takes a lot of discipline to stay focused on the future. That said though, there are way more opportunities in the Silicon Valley and in the US for women in tech to be successful, provided one is willing to work hard.

Do you think things are changing for the better? What progress do you hope to see women achieve in STEM fields over the next 10 years?

I think things are changing for the better as more and more economic opportunities are created in high-tech. This generation definitely thinks differently than its predecessors about gender roles and equality in science and tech. I personally hope to see a much larger percentage of STEM workforce occupied by women in the next decade. Intellect is not gender-based, and it’s a shame to have such low participation of women in science and technology. Higher participation could mean more fulfilling careers for women, accelerated scientific progress, and increased productivity for the entire nation.

What do you think will be the single biggest factor in propelling that progress? I think changing how girls are brought up from a young age is an extremely important factor. Getting young women interested in science and technology early on, teaching them practical skills, and giving them more responsibilities will make them both interested and confident to pursue a career in STEM. A lot of the disadvantages that women in tech face today will lessen if more women study and work in the scientific fields.

What advice would you give to Women in Tech?

Talent is what matters on any good team. Long-lasting success is built on deep, practical skills. Try to understand the fundamentals of any field you pursue, think long-term, harness your strengths, and don't aim to please everyone. If you have to work extra hard, so be it getting efficient at learning is an extremely useful skill to gain.

How does one become successful in the field of Data Science? Data Science is a field that encompasses a variety of roles, ranging from the more technical to the more business-oriented. The technical roles can be either machine learning/data-mining or data-engineering heavy. I have worked in roles that have spanned multiple responsibilities. What you pursue should be largely determined by your interests and skills. For a machine learning role, it is essential to have strong fundamentals in linear algebra, statistics and optimization. Machine learning is a rapidly evolving field, so staying on top of state of the art techniques, and being aware of the myriad of emerging applications is absolutely helpful. For a data-engineering role, you need to be a good programmer, and be skilled at efficient data manipulation and storage. For a more analytics-oriented role, you really want to understand the product domain you are operating in, and think like a business owner. Most analytics roles also require good engineering skills for data manipulation and predictive modeling. A common mistake that many data scientists make is to get too carried away by data and definitions, or spend too much time on unscalable implementations. While it is essential to accurately understand and interpret your data, at the end of the day, data is only as good as the actionable insights it produces or the business metrics it moves.

What's next for you? I’m really interested in entrepreneurship, as you can tell from my background. Someday soon!

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