What’s the future outlook for this space?
I believe the high demand for data science will continue into this decade. Thanks to the powerful technologies that can collect and transmit data from devices anywhere in the universe, data volume and velocity have increased exponentially. This has enabled data science applications in industries across many professional areas- from oceanography to credit fraud detections to healthcare providers, product development, behavior predictions, video, and voice interpretations. The applicable list goes on and on.
Today, organizations across the private and public sectors are scrambling to come to grips with data’s transformative potential. The investment and effort required to build the necessary know-how are huge. With the continued growth in data science applications will come the continued demand for data science-related roles such as data scientist, analytics software engineer, data engineers, machine learning specialist, AI/cloud-based solution providers, and anyone who knows how to handle and make sense of the data.
What's the current demand-supply gap?
In the past decade, the skyrocketing demand for data science and business analytics has forced educational institutions to modify existing curriculums and add curriculums for data science and business analytics. In addition, a lot of data science-related job training boot camps sprung out all over the world. As a result, it is easier now to fill entry-level positions. Finding professionals with advanced analytics experiences and skills or those who know how to tell a meaningful story with the data is still extremely difficult. It is taking businesses a much longer time to fill these advanced positions. Across all industries, organizations pay premium salaries to those with the right expertise.
What's the state of gender diversity in Data Science? What can be done?
The gender gap in the professional world is slowly closing, but unfortunately, there still is a daunting gender gap in Data Science. Based on BCG (Boston Consulting Group) 's online publication in March 2020, only ~15% - 20% of Data Science professionals are women. It's hard to sort out all the factors that have contributed to the gap, and it is equally hard to quantify the impact of each of the factors. However, organizations need to have actionable measures to correct their gender imbalance. Educational organizations need to make data science more tangible, available, and relatable to female students. Meanwhile, companies can implement strategic planning to reduce hiring bias. In both cases, I believe companies such as yours – Meytier, can play a significant role in helping organizations close the gap.
Tell us a bit about your personal story - how did you build your career in data science?
I’ve spent 22 years in this field. For the past 20, I have been leading a team providing marketing analytics services and building strategic direct marketing programs for many top 10 P&C Insurance carriers, Fintech companies, and new start-ups. I’ve gained deep experience in using data to target the right prospects for any given brand to cost-effectively market Auto and Home insurances, Life insurance, Health insurance, car warranties, or personal loans. The most rewarding part of my career is to get to experience the success of marketing campaigns with clients. That success is mainly due to predictive models and data-driven marketing strategies. It is equally rewarding to see how everyone on my team has grown professionally. Some who joined me right after graduate school are still here. I’m proud to tell you that at EXL, 69% of our marketing analytics teams in India and the US are women.
I didn’t understand the power of data and statistics until I took my first graduate Econometrics course under Dr. Carter Hill at LSU in 1997. I was fascinated that one could explain what elements are driving trends and how significant each element is. When I began studying Marketing and Finance, it became evident that the business world can benefit greatly from data and statistics. As an international student from China, I had no idea what job titles to look for and how to show my skills and interests on a resume correctly. When I graduated with a Master’s degree in Economics in June 1999, I reached out to Smith Hanley for help with a job that would predict business outcomes rather than economic outcomes. I was told I was looking for either a Decision Analyst or a Business Analyst. They got me one interview with Advanta Corp, one of the most prominent Master card issuers in the 90s, and that was my start.
During the first few months on my job, I focused on strengthening my statistical language programming skills and experimenting with predictive modeling techniques. I thought that focus was necessary if I wanted to stand out among 20+ analysts on the team. However, I soon realized the limitation of my career growth if I focused too much on the technical side. I was new to the country, the culture, and English is my second language. I felt my career would be limited if I didn’t expand and master other skills like effective communication and business acumen. I asked my supervisor if I could pursue a degree paid for by the company. It was brave for me to ask, and the next thing I knew was, in the seventh month of my job. HR allowed me to enroll in a master’s degree in English literature program at Acadia University. It’s always good to advocate for yourself. The worst anyone could say is no.
After about 18 months on the job, the VP of the business unit left Advanta to start his own business with a few of his friends, and he asked me to join his firm to build up the marketing analytics capabilities. I realized the potential learning opportunity a start-up could provide and took the job. That move paid off and prepared me to become a seasoned, business outcome-focused data science leader.
What do hiring managers look for in Data Science Interviews?
Speaking from my own experiences: The answer depends on the level of positions to be filled. For the entry-level, strong skills in computer languages and relevant business analytics/statistics courses are a must, and some hands-on experiences are always a plus. For mid-level managers who have been in data science for a number of years, the focus is on projects and people managing skills, communication, and storytelling.
What can non-tech people do in the field?
I believe non-tech people can still play important roles in leading and managing data science and AI projects, as long as they understand what it takes and how it works. A good analogy would be how CEOs lead companies. You don’t need to know how to do the technical side of data science as long as you understand how it works and can be implemented.
Any advice for people looking to make a mid-career move to Data Science?
Changing to data science roles in mid-career is very doable and could be rewarding; it just takes commitment. There are a lot of free online sources and boot camps for anyone to acquire the necessary skills as the starting point.