Top Women In Machine Learning And Data Science
Ever wondered how many roles in the computing sector do women hold? What’s the percentage of women in Machine Learning and Data Science?
Unfortunately, not many! According to the IBM tech reports, women make up 20% of the total tech roles across the globe.
The Data Sector has seen an unprecedented boom in the last few years. Data Science, Machine Learning, and Data Engineering are amongst the sectors that have disrupted the functioning of enterprises and have taken them to a new level altogether.
Data has become the driving force behind transforming the Tech industries and catalyzing innovations.
Thus, this has resulted in an increased demand for Machine Learning Engineers, Data Scientists, Data Engineers, etc.
So, how many women are there in Machine Learning?
Only 12% of the female workforce is working in the Machine Learning sector.
What about Data Science? How many female data scientists are there?
26% of women hold a position as a Data Scientist.
That’s a worrying statistic about women in Machine Learning and Data Science. Surprisingly, women are still underrepresented in the technology sector, and their efforts often go unnoticed.
In this article, we will be sharing the profiles of amazing and inspiring women that work tirelessly to contribute to the Tech sector.
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Carrie Grimes Bostock is a Harvard Graduate who has a keen interest in exploring quantitative methods to deal with disparate data.
She completed her Ph.D. in Statistics and is working in Google as a distinguished AI Engineer since 2003.
Her responsibilities include planning data-driven resources, analyzing costs, and distributed cluster management.
Nikita Johnson is the founder of an organization names “RE•WORK.” The majority of the workforce of this institute constitutes women.
It aims at organizing events that help amalgamate the latest technologies, science, and entrepreneurship.
Jennifer Chayes is a prominent Scientist and Managing Director at Microsoft Research where she co-founded and led three interdisciplinary labs.
She is one of the inventors of the field of graphons that are used widely for the Machine Learning processing of large-scale networks.
She holds a Ph.D. in Mathematical Physics from Princeton University. Her recent workings focus on ML applications cancer immunotherapy, ethical decision making, and climate change.
Jana Eggers
Jana Eggers is the CEO of a Neuroscience-AI specialized company called Nara Logics.
She founded this company with a team of 3 people, which now consists of whooping 50,000 people.
Her company provides a platform for recommendations and decision support.
Caitlin Smallwood is the Vice President of the Science and Algorithms vertical at Netflix. She leads a group of professionals that consists of mathematicians, data scientists, and statisticians.
The group works in the domain of predictive modeling, algorithm research, and data analytics.
Summing Up – Women in Data Science and Machine Learning
There are many other notable women in Data science and female Machine Learning Engineers.
While the technology industry lacks a female workforce, they are making efforts to become more inclusive and present opportunities to women. Data Engineering is another newly emerged field that demands more employees in today’s dynamics.
Women in Data Engineering or in general in any data-related field can be powerful allies in shaping the future.
Although the number of women in Machine Learning and Data Science is minor, they have made notable contributions in the Data field over the years.
Many organizations are now making it their mission to diversify the workforce and hire without any gender disparity.
Many women have also taken the matter into their own hands and are working tirelessly to create opportunities and networks in the Tech field for other females.