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The shortage of women in tech isn't just a "diversity" problem, it's a social one. Kindra Cooper had talks to four women in tech about their experiences working in four major areas of the tech industry.
Women are still vastly underrepresented in #STEM, resulting in a world where vehicle safety features are designed for men's bodies, women are 50% more likely to be misdiagnosed for heart disease, and recruitment AI algorithms lock women out of certain jobs.
In the midst of a national discussion about whether the tech industry is welcoming to women, we're plugging into the local community and asking what's top of mind for women in tech.
Were joined by Alison Cossette, a Data Scientist at Remedy Partners. Also by Julie Lerman, a longtime coder who shares her expertise by speaking at conferences and mentoring development teams around the world. And by Maureen McElaney, founder of the Burlington chapter of Girl Develop It and a Developer Advocate at IBM Cloud Data Sevices.
Metis hosted an Ask Me Anything session on their Community Slack channel with Alison Cossette, Director of Data Science in Research and Development at the NPD Group and instructor of our upcoming Introduction to Data Science course.
"I am known to say 'numbers are the best story-tellers,' and it's our job as Data Scientists to give voice to those stories," said Cossette. "We enable others to make sound business/policy decisions with confidence because of our attention to detail and commitment to quality modeling outputs."
Officially, Alison Cossette is a data analyst for the University of Vermont Medical Center. Unofficially, she says proudly, "I'm the resident data nerd."
Alison Cossette uses her analytical mind examining Vermont health network's data on a big-picture level — for example, combining patient, location and population data sets to anticipate future demand for services.
Alison Cossette talks with Interviewer Rajib Bahar and Shabnam Khan about her Data Science journey, Leveraging Linear and Logistic Regression, Why Python, Defining AI and Machine Learning and finally the responsibility of building ethical algorithms.