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Is There a
Diversity Problem
in Data Science?

The Challenge of Diversity in Data Science

introduction.

Data science is a field that's been growing exponentially over the past few years, and with it, a changing demographic in the workforce. As more tech companies are opening their doors to a more diverse talent pool, it begs the question of whether recent DEI programs and similar efforts are producing the outcomes they committed to offering. The success of these initiatives will rely on more than just good intentions – it will require dedication to inclusivity and a clear understanding of the challenges that stand in the way.

We all know that diversity is important in any field, and data science is no exception. A lack of diversity can lead to missed opportunities, as well as threatens decision-making and creativity. At present, there is a significant lack of black data scientists in the industry, and this problem has been well-documented by both the National Science Foundation and The Wall Street Journal. Diversity not only inspires a more inclusive environment but also leads to better discoveries and insights. Companies that are genuine about their company culture and data science ambitions should be working towards overcoming these challenges, with or without the command of DEI initiatives. 

The effort to usher more people of color and women into the tech industry is respectable. Twitter, for example, attracts many data practitioners who are excited to share up-and-coming opportunities for working professionals. Many of these accounts draw audiences from communities of color and are often the person's main resource for upskilling into a career in tech at no cost. While the 'twitterverse' doesn't exactly represent the population, the fact opportunities (particularly in data science and related fields) available for disenfranchised communities are readily shared on one of the most popular and powerful social media platforms, brings some light to the end of a long tunnel.

In fact, many of the same scholarships and programs are promoted on Instagram, giving more mileage to outreach efforts and increasing the probability that the audience they are designed for is reached. Sending well-intentioned notes into the ether was never an option. Instead, these targeted ads and heavily engaged tweets promote paths into tech careers for BIPOC and women that historically have been dominated by white males. While it's too early to determine whether these programs can be linked to more diversity in the tech talent pool, the conversations that appear on these social channels may serve as anecdotal evidence of what companies should expect and ultimately, embrace.

However, an abundance of resources alone isn't indicative that significant change has reached the workplace. In fact, it can be misleading. Recent data concludes within the past 7 years, the proportion of black professionals in technical roles had grown only 1%, between 2014 and 2021. This number falls well short of what would be necessary to reach parity.

This isn't to say that the current climate is hopeless. There are some encouraging signs including a 44% increase in black women entering the field of data science from 2014-2016 and an 11% growth between 2016-2018. But, without more significant strides in this area, it will be difficult for data science to reach its full potential.

While DEI initiatives play an important role in diversifying the data science landscape, their effectiveness depends on a number of factors, including the dedication and engagement of those who receive them. Companies that want to be genuine about their company culture and ambitions should aim to do more than just bluewashing their landing pages. They should be working towards developing a pipeline of diverse talent that can contribute to innovation in the field.

The good part is, tech companies don't even need to reinvent the wheel. Scholarship programs, such as Adobe Digital Academy, create a simple, but effective roadmap for its participants – from student to paid intern, to eventual employment at Adobe (note: internship and employment are not guaranteed). Participants who complete the training are either placed in a paid internship or take their newly acquired skills and a professional certificate from partner, General Assembly, where they can compete for more advanced positions. 

Startup BondCliQ offered a similar experience. Selected participants of their BEST cohort participate in live Zoom classes, complete homework, and simultaneously complete various tracks on the DataCamp platform. Those who make it to the second phase go through a much more intense, full-time learning environment but are paid. After successful completion, they may be invited to work at the company. Models like the two aforementioned can be replicated without costing a business too much in resources. And their outcomes are transparent.

 

conclusion

To be successful, DEI initiatives will need to go beyond simply hiring a diverse group of individuals. They'll need to focus on creating an environment that celebrates differences and encourages creativity and innovation. If companies are serious about their diversity goals, they'll need to take a critical eye at their current recruitment practices and ensure employees of diverse backgrounds are not just hired but integrated vertically into lucrative roles.

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Maths + English is a personal blog documenting my journey into data science. Topics I cover include ethics & data, machine learning, data analysis, business intelligence, and behavioral design,

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