Interview with Cornelia Lévy-Bencheton, Principal of CLB Strategic Consulting, LLC

Interview Questions

[OnlineEducation.com] Traditionally, there have been a number of routes people have taken to a career in analytics – computer science/engineering, math/statistics, MBA programs. What was your entryway to as career in data and analytics?

[Ms. Lévy-Bencheton] “Why” is my word. Solving for “why” has been an existential imperative for me. This line of questioning throughout my career as a financial services and marketing professional pointed me in the direction of metrics, measurement, and analytics – to account for the “why.” During my career, I have been head of research and chief strategist at three well known financial companies and, in each situation, I solved for the “why” of marketing challenges using techniques such as benchmarking, test and control, segmentation, next likely product bought, covariance analyses, conversion path, cross channel attribution, decision support algorithms, data-driven decision support systems, and many others. My formative educational experiences include an MBA, a Digital Marketing Certificate, and the numerous courses I’ve taken to keep current in my field. With my clients, I help them get to “more,” whatever more means to them, by asking “why.” I’m known as “Miss More.”

[OnlineEducation.com] Did you study analytics as an undergraduate or at Stanford?

[Ms. Lévy-Bencheton] At Stanford, I stopped at a Master’s in French and Italian on my way to a Ph.D. This was actually a very analytical experience. Analyzing the ins and outs, themes, sociological, historical, and psychological implications and context of literature, as well as the engineering of novels, plays, poetry et al, is actually quite analytical. Of course, having a start in analytics for a future in the analytics field would appear to be good. I say “appear” because a liberal arts/business background provides a greater context and platform for an analytics degree. I’d vote for a broader background at the bachelor’s level​ and then a master’s in an analytical discipline for later on. My own entry into the field was more of an on the job experience of finding something I absolutely love.

[OnlineEducation.com] Is it your impression that women are underrepresented in the field of analytics? And, did you feel that analytics was an atypical career choice for women when you entered the field?

[Ms. Lévy-Bencheton] When I started in this field, women were beginning to come on stream, having careers as well as families. There was excitement and buzz around new challenges and chances to succeed. But, yes, women are underrepresented in the field of analytics and STEM. And, yes, I have found this to be the case. It’s more than an impression. Women are actively pursuing advanced degrees in higher education and are graduating at a higher rate than males. However, it is important to note that it is not the number or percent of degrees awarded to women that is an issue. Rather, the field of concentration or STEM subject is where there is a lag. According to the US Census Bureau, U.S. women made up 27% of STEM jobs in 2011 and 34% of STEM jobs in 1990. That is not going in the right direction.

I also do believe that analytics as a career choice is atypical in our culture. This is not the case elsewhere. For instance, for bachelor’s degrees in STEM, we know that the USA has 31%, Japan has 61%, and China has 51%. The USA needs STEM competency for our economic growth and success on the world competitive stage. And we need to bring women into more active and productive participation in our economic success. The bottom line is that women are making up a larger and larger percentage of our workforce and in the pool of educated professionals in the U.S., but fewer and fewer have chosen to work in technology. Arguably, the tech industry is the most important growth engine for the economy and the most important source of innovation across every industry. Women are underutilized in STEM fields and are a vast, untapped talent pool that can help meet our needs.

[OnlineEducation.com] In your view, are there particular challenges faced by women in analytics, and what are they? Conversely, are there unique opportunities for women in the field? I’m particularly curious what you learned through your work in the book Women in Data.

[Ms. Lévy-Bencheton] This field needs an image makeover. In our culture, analytics and the STEM disciplines are often seen as unfeminine and the purview of males, the proverbial boys’ club where only the boys can thrive. This makes many women turn away before they even start or have a chance to explore. We need to promote STEM career choices to women from a benefits perspective. Making the industry as a whole more attractive to women is partly a marketing and branding challenge, the goal of which should be to help women go down the path of working in a field they love.

STEM fields are highly creative. Problem solving is paramount, and this can be very satisfying and lots of fun. For those who have the curiosity to pull things apart to find out what makes them tick, problem solving, independence, and opportunities to explore are all real benefits.

Women have much to offer. They are great communicators and are detail oriented. They relate well at all levels and can provide the relationship skills for understanding between cross functional tech and business teams. Here are a few practical reasons why data science and STEM can be a great field for women:

• Reasonable or great pay
• Interesting work
• Speaking and/or writing engagements
• Networking with interesting colleagues and exchanging stimulating ideas
• Flexibility to meet the demands of family, balanced with a social life and friends
• Opportunity to exercise hard-won skills and education
• Having an impact on the state of knowledge in the field
• Chance to influence the next generation of talented women
• Staying relevant in the field of expertise.

Being smart is in. Women are poised to be on the next generation of impactful influencers in business and science.

[OnlineEducation.com] In the interviews you did for Women in Data, I believe you encountered what might be characterized as an evolving attitude toward women among millennials in the data world. How would you describe that?

[Ms. Lévy-Bencheton] Millennial women are more open to STEM career possibilities than their older sisters. In my book Women in Data, I discovered a “growth mindset” shared by those I interviewed. These women were open to learning. Even setbacks were considered a learning experience. A “set back” is nothing but a “set up” for future learning and moving forward. The only limitations on success they see are self-made. There is no hesitation among this group to find mentors and sponsors. Asking for help is considered a strength. They are not shy. They thrive on challenge, do not balk at detailed work, have uncompromisingly high standards, and can deliver the most brilliant and elegant solutions to dogged problems.

Two other factors are important to note. There is better parenting in general these days and more support, encouragement, and advice from mentors and sponsors, which is making this growth mindset become a reality. Also, their male millennial counterparts are more open, accepting, and respectful of women in the field.

[OnlineEducation.com] What advice would you give to women who are considering a career in analytics/data science? I’m interested both in skills and training, and also in issues surrounding the professional culture in the field.

[Ms. Lévy-Bencheton] Among the rising stars I interviewed, there was some disagreement as to whether or not coding was absolutely mandatory. Some thought it could be outsourced or assigned and need not be considered a must. All agreed, however, that a data scientist should know how to code because it affords independence and efficiency, which are distinct benefits to getting the job done quickly. This is definitely an area where you need brains and special analytical training to excel. Educational preparation is critical.

My sense is that opportunity abounds. Keep an open mind. There are many variations and specialties within the data science field and many paths for career development. This is an important consideration for women returning to work after a career break for child rearing or eldercare. Women in STEM can jump back into the workforce, leveraging their skills in new ways for re-entry without the fear of being left behind. Another strategy I would recommend is to find mentors and sponsors and to network like crazy where there are role models, examples, advice givers, and influencers who can offer solid help.

STEM fields can provide rewards disproportionate to certain other fields for women. There is much ado about women being paid less than men. In considering a career in analytics and STEM, entry level women are paid a premium and earn incrementally more than their male counterparts.

For example, when women do make it into the STEM fields and stay, their earnings better many others. Women with STEM jobs earned 33% more than comparable women in non-STEM jobs – considerably higher than the STEM premium for men (11%). As a result, the gender wage gap is smaller in STEM jobs than in non-STEM jobs. This might be big news for many people.

Specifically, STEM degrees entitle holders to a premium. College-educated women (regardless of choice of undergraduate major) earn 20% more in STEM jobs than elsewhere. This is nearly double the 11% premium that college-educated men realize working in STEM fields. Female STEM degree holders on the other hand, earn 9% more than women with other degrees regardless of their job:

Source: Women in STEM: A Gender Gap to Innovation. by David Beede, Tiffany Julian, David Langdon, George McKittrick, Beethika Khan and Mark Doms, Office of the Chief Economist. US Department of Commerce, Economics and Statistics Administration. August 2011.

[OnlineEducation.com] Are there particular areas or specializations within the field that appear to be more accommodating to women? Or, are there areas within the field in which women simply appear to be making greater headway?

[Ms. Lévy-Bencheton] My experience is in the analytics side of financial services and that is, like STEM, a male dominated field. But women are making headway – I believe – in all these areas. There is a concerted effort to focus on the disproportionality of the situation and to create incentives for women with children. There are many resources being applied to fixing the things that did not work well for women in the past.

Many say communication is the “female DNA.” And, communication is paramount for success in the data scientist role. Given that, data science may favor women, and women who cultivate strong communication skills may have an edge in these jobs. When a consultative approach is needed together with a high level of collaboration among and between work groups – both technical and nontechnical – and up to and including the C-Suite level, women may have the social savvy and relationship skills to step in and get ahead of their male counterparts.

[OnlineEducation.com] I’ve seen you allude to certain remedies for closing the gender gap in analytics. Can you elaborate on that?

[Ms. Lévy-Bencheton] From my interviews and observations, here are a few practical ways of closing the gender gap and shattering the glass ceiling in analytics and STEM.

• Joining professional women’s organizations (like the FWA) or other women’s groups and networking with women who can provide solid career help and advice as well as provide emotional help, as role models and examples.
• Build the depth of network relationships you need. Not an easy process. You need to work at it.
• Keep your skill level fresh and on the front burner. Ever evolving technology creates new and interesting work if you keep your eye open. You need to speak today’s tech language.
• Be perseverant.
• Stand up to bullies.
• Don’t ignore communications and presentations skills. Voice, dress, posture. Women need to work on their whole package and presentation.
• Avoid complaining, as negativity is contagious and counter-productive.