Customer obsession runs deep at Intuit.
To deliver the kind of relevant, seamless, and high-value experiences our customers expect, we need to be able to understand their needs and perspectives. In fact, empathy is the foundation of the design-thinking principles that guide our data scientists in product development. That’s one of the reasons we place such importance on diversity and inclusion. The more our organization represents the full breadth of our customer base, the better we’re able to deliver on our mission to improve prosperity for people of all kinds.
What do data scientists do?
Many outside of the analytical field aren’t sure what do data scientists do? Data scientists are responsible for analyzing and interpreting data. They use this data to help their company make better decisions, understand customer behavior, and improve products and services. In order to do this, data scientists need to be able to not only analyze data but also understand how it relates to the business they are working for.
What are some main data scientist skills?
There are both technical and interpersonal skills that data scientists need in order to be successful. The technical data science skill sets include:
- Analytical Skills: Data scientists need to be able to take large amounts of data and analyze it in order to find trends or patterns.
- Business Acumen: It is important for data scientists to understand how the business and numbers that they are analyzing translate into successful business opportunities.
- Programming Skills: Data scientists need to be able to write code in order to manipulate and analyze data.
- Statistical Analysis: Data scientists use statistics to understand how different factors are affecting their data.
Expanding the data science skill set
People often only think of data science skills in technical terms (i.e. computational mathematics, engineering, and data analysis). “There’s another side that’s just as important,” Kavita Sangwan, Intuit’s Director of Technical Programs, Artificial Intelligence and Machine Learning says. “You also need skills around being a team player, collaborating, empathy, and communication. It’s important for us to operate in a team setting. A data scientist has to interact with a product manager, a data engineer, a business person, a legal person. We need the ability to have those conversations the right way in a cross-cultural space.”
Why Does Diversity in Data Science Matter?
A recent study has found that around 15% of data scientists are women, with 3% of that number being women of color. In this past, young girls and women have been pushed out of more scientific, mathematical, and technological roles for men. Female data scientists are critical when looking for more diverse actions and insights.
As part of our commitment to increasing diversity – not just at our own company, but across the tech industry – Intuit was a top-tier Global Visionaries sponsor for the recent Global WiDS (Women in Data Science) Conference 2019. Held at Stanford University in tandem with 150 regional events, this year’s WiDS Conference attracted more than 100,000 participants around the world to celebrate and advance the work being done by women in this revolutionary field.
However, we recognize there is a lot of work to do because it’s not only women and women of color who are underrepresented, either. Fewer than 20% of jobs in the STEM field are held by those who identify as part of the LGBTQ+ community.
There is a growing demand for data scientists, and with that growth comes the opportunity to increase diversity within the field. A more diverse data science workforce will lead to better insights and solutions. It’s been shown in study after study that diversity in the workplace makes us smarter, more creative, diligent, and harder-working. While data-driven and analytic jobs have predominantly been white men, there are many reasons why added diversity from women, the LGBTQ+ community, and people of color can make a difference in the field. One study showed that teams with more women are more productive and have better ideas. And, people of color often bring unique perspectives to data-driven decision making due to their cultural experiences.
Moving the needle on diversity and inclusion
Representation shouldn’t be constrained by rank—especially in an industry where women hold so few leadership positions. “It’s important for us to empower and grow the people we hire by providing training opportunities in areas such as executive presence, negotiation, and public speaking, as well as helping advance their tech skills,” Kavita says.
In a study taken from the American Association of University Women (AAUW), an organization dedicated to women’s advancement in STEM-related careers, the results show:
- 21% of engineering majors are women.
- 19 % of computer science majors are women.
- The white, male-dominated culture of these jobs make it hard for women and people of color to enter the field.
- 38% of women who majored in computer science work in their field, with only 24% of women who majored in engineering work in their field.
- Latina and Black Women in STEM earn around $33,000 less than their male counterparts.
Intuit’s opportunities—and responsibilities—as a data science-driven company
Intuit has invested heavily in recent years to embed AI and ML across our product suites and technology platforms. By making our products smart, we can give our customers more personalized tools to get more done with less work, save money, and achieve their financial goals.
Of course, the vast opportunities offered by data science also come with an important responsibility. As we make greater use of customer data—always with their consent, of course—we must also maintain their trust. “We operate with the mindset that this is the customer’s data, and we want to be the best stewards we can be as we use it to build smart products for them,” Kavita says.