Women in Data Science: Challenges Faced and Opportunities

Data^[4] Science has been one of the most rapidly expanding and promising career paths. Data scientists are at the forefront of innovation, particularly in emerging technologies like machine learning^[2] and artificial intelligence^[1]; countless industries depend heavily on vast digital datasets to extract valuable insights. The Harvard Business Review called data Science the “Sexiest job of the 21st century” (Davenport, T. H.)1 in 2012. Even after a decade of being hailed as this, the representation of women in the Data Scientist roles is still a concern, with their numbers potentially decreasing despite their majority in the labor force.

The U.S. Bureau of Labor Statistics predicts a rapid 35 percent growth in employment for data scientists by 2032, exceeding the average growth rate for other professions. Despite this potential, more women need to be in data science. Encouraging more women to pursue careers in data science is crucial to addressing this gender disparity.

In this article, we will delve into the following reasons:

1. Why Data Science is an exceptional career choice for women
2. The Challenges Encountered by Women
3. How to Conquer These Challenges
4. The prospects in this field

1. Why Data Science is an Exceptional Career Choice for Women

Women play a critical role in data science, which is in high demand. Despite progress in promoting diversity, there is still a gender imbalance, with men making up the majority of data scientists.

A career in data science opens abundant opportunities for women. Whether entering this field as a novice or transitioning as an experienced professional, numerous prospects await those possessing the requisite skills and experience.

Beyond just the money- like the average yearly pay for a Data Scientist at USD 121,114- working in data science brings many opportunities. You can move up in your career, work with the latest technologies, meet experts in different fields, and gain skills you can use in any industry. This field has much room for growth, and women can succeed in data science.

Firstly, this is a highly sought-after field with a high demand for professionals. Many organizations across different industries use data science, leading to numerous job opportunities.

Secondly, data science provides significant job satisfaction. As a data scientist, I know individuals face complex business and societal challenges. The satisfaction of solving these intricate problems is unmatched, making data science a captivating and rewarding career choice for women.

From a business standpoint, prioritizing women’s participation and promoting inclusion brings valuable diversity to decision-making processes. Women’s unique perspectives and skills are crucial for the growth and success of the data science industry.

• Companies with a female representation exceeding 30% (belonging to the top quartile) are likely to outperform those with 30% or fewer women financially.
• Reducing the gender gap by 2025 could result in an additional USD 12 trillion in global GDP (Woetzel, L., Madgavkar, A.)²
• An inclusive organization is twice as likely to surpass financial targets, three times more likely to be high-performing, six times more likely to be agile and innovative, and eight times more likely to achieve superior business outcomes.

Despite the initial challenges women might face, data science offers an excellent career path for upcoming and established women in technology.

2. The Challenges Encountered

The representation of women in data science is a complex issue that needs to be addressed together. Even though women comprise 57 percent of the workforce, only 27 percent are in the technology industry. Over 50 percent of women in this field are likely to leave before age 35, and 56 percent are inclined to go in mid-career. The gender gap in technology and data science has become a bigger problem over the past two decades, raising concerns about inclusivity in the industry.

• Multiple contributing factors to this phenomenon include:

   

  • There is an inherent bias created by the outdated marketing narratives that portray tech as a male-exclusive domain^[5], which, by default, discourages women from potentially thinking about a career in data science. Moreover, the lack of early exposure to computing skills like coding further widens the gap, as highlighted by prominent figures in interviews with women in data.
  • Confidence issues may also play a significant role, with studies showing that women often underestimate their programming skills compared to their male counterparts. Stereotyping further hinders women’s perceived ability for success in the field, leading to self-imposed limitations.
  • The challenge of gender diversity in tech and data science goes beyond educational exposure. (Avolio, B., Chávez, J.)³ Recruitment and attrition present additional obstacles, resulting in a decline in women in the field over time.

3. How to Conquer These Challenges

Here are some essential tips that will help you if you are starting your career as a data scientist:

• • Identify your learning goals: Determine the specific data skills you want to learn, whether just writing your first Python code or trying to learn SQL^[7].
  • Choose the right resources: Select courses, tools, and languages that align with your goals.
  • Create a learning schedule^[3]: Allocate time for practice and learning.
  • Apply your skills to real-world projects: Work on practical tasks to gain experience.
  • Connect with supportive people: Find mentors, role models, and advocates who can guide and support you in your journey in data science.
  • Use your strengths: Align your career with your strengths and values to find fulfillment in data science roles that match your aspirations.
  • Expand your network: Building your professional network is essential for gaining valuable insights, collaborating with peers, and accessing various growth opportunities.
  • Advocate for fair pay: When negotiating your salary, be confident in seeking fair compensation based on your skills and contributions in the data science field.
  • Challenge stereotypes: Learn to challenge stereotypes and biases to create an inclusive and empowering environment where everyone can thrive in data science.

4. The Future Outlook

The future of women in data science relies on their presence and success in executive and leadership roles. To achieve this, women in data science need both education and confidence, crucial elements often discussed in the context of the gender gap in technology. Beyond issues like male-dominated cultures and mentorship gaps, researchers now emphasize confidence’s significant role as an obstacle for women in statistics and data science.

As data plays a central role in every sector, a degree or certification allows individuals to pursue careers aligned with their interests. In this evolving landscape, being part of the data science field is not just about working with data; it’s about actively shaping its future.

Conclusion

The growing amount of data in various industries highlights the urgent need for more data scientists, especially women, to harness its potential. As mentioned, data scientists play a crucial role in analyzing, interpreting, and communicating valuable insights from large datasets. By promoting gender diversity and encouraging more excellent representation of women in the field, we can incorporate various viewpoints and ideas, fostering innovation and effectiveness in solutions.

Endnotes

¹ Davenport, T. H. (2024, March 19). Data Scientist: The sexiest job of the 21st century. Harvard Business Review, https://bit.ly/3Ywkgyb

² Woetzel, L., Madgavkar, A., Ellingrud, K., Labaye, E., Devillard, S., Kutcher, E., Manyika, J., Dobbs, R., & Krishnan, M. (2015). The power of parity: How advancing women’s equality can add $12 trillion to global growth. In McKinsey & Company, https://bit.ly/3Yqlk6J

³ Avolio, B., Chávez, J., & Vílchez-Román, C. (2020). Factors that contribute to the underrepresentation of women in science careers worldwide: a literature review.Social Psychology of Education, 23(3), 773-794, https://bit.ly/46EV7TZ

Sahithi Jukkalkar

Sahithi Jukkalkar works as a contractor and senior data analyst with AT&T. She started her career in 2017 as a data analyst with Capital One. Sahithi has been a consultant since 2017 and has worked for clients like AT&T, Capital One, Nike, and Kroger in the roles of data analyst, data scientist, and senior data scientist. Jukkalkar worked on product recommendations at Kroger, Nike, and Bulit ML models, enabling customers to make purchases based on their interests. She has also led effective marketing campaigns using A/B testing^[6] to understand customer pulse. She has worked with large datasets and developed KPI^[8] dashboards for my stakeholders. Sahithi is a self-professed nerd for continuous learning and aspires to be an author. She aims to help women in a way that makes them financially independent. She takes Sudha Murthy as her inspiration and desires to work in philanthropy and help girls and women live better lives with good education and financial freedom. Sahithi Jukkalkar can be reached at jsahithi13@gmail.com.

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