5 Questions with a Woman in Data Science — Beth Partridge
By: Berkeley I School
Tags: Ischooldiversity, Women In Data Science, Uc Berkeley, Women In Tech, Data Science

5 Questions with a Woman in Data Science — Beth Partridge

MIDS alumna Beth Partridge shares her journey as data scientist.

The School of Information is proud to highlight our intelligent I School women working in data science. MIDS alumna Beth Partridge and current CEO, Founder, and Chief Data Scientist at milk+honey, shares her experiences with data science.

What influenced you to pursue a career in data science?

I spent the first 20 years of my career as an electrical engineer at Intel. Semiconductor fabrication requires incredible levels of precision and accuracy and being data-driven just became part of the corporate DNA. I didn’t even realize that the rest of the business world wasn’t the same way until I left to run a small technology consulting company following Intel. It drove me crazy to make any business decision without some level of quantitative analysis. So, when the whole “Big Data” craze first emerged I was thrilled.

What work are you doing now?

When I was nearing the end of the MIDS program and began exploring job opportunities for data scientists, I was struck first by the amount of misinformation in the business world about what ML/AI is. I realized that most businesses outside of the data-native giants were trying and failing to embrace it. As a result, I started a consulting company called milk+honey to bridge the gap between data science and the business world.

How did what you learned at the School of Information develop your skills and further your career goals?

I gained practical experience with much of the current data science technical ecosystem, the skills to create all of the major types of ML models myself from the ground up, and perhaps most importantly a deep understanding of just how critical it is for every business to embrace and develop ML as a core competency.

The MIDS program gave me the data science foundation that I needed to embark on this journey.

ML is to business intelligence what the microscope was for medicine, the telescope for astronomy. ML opens a whole new world of analytics, where instead of settling for group statistics and estimates, we can see, measure and accurately predict outcomes for every single individual element of a population. It’s a game changer that will separate the winners from the losers. Our goal is to give as many of the non-data-native businesses a fighting chance as we can reach.

Who inspired you when you were young?

For better or worse, I don’t ever remember making the decision to become an engineer. My father was one of the original tech entrepreneurs before Silicon Valley even existed. He wanted me and my two sisters to all follow in his footsteps in engineering. (He got two out of three.) I’ll never know what path I would have chosen without that expectation. I have strong interpersonal skills unrelated to the technical aspects my career. But looking back it’s the combination of the two that enabled me to survive and eventually thrive in a very male-dominated field. I have never regretted having the engineering foundation but there were rough patches for sure.

What advice do you have for young women who want to work in the field of data science?

There’s no denying that there are still impediments to success in tech for women that don’t exist for men. Higher initial barriers to entry, greater initial scrutiny and workplace cultures that are not female friendly are all high on the list. Many of the barriers are being addressed and shifting slowly, and there are many resources for support and advice available like online forums, formal organizations and mentoring opportunities.

But being a woman can also have advantages. For example, in the absence of gender-driven competition I think I was able to establish deeper, more lasting interpersonal working relationships than my male counterparts could. Those relationships grew into a strong and loyal network over the years that provided support and opened doors.

There are aspects of data science that necessitate stronger soft skills than technical jobs have historically — skills that women are more often culturally encouraged to develop than men. For example, one of the most common roadblocks to establishing a successful data science program is getting historically siloed groups inside of companies to share data and work together. Getting past this roadblock often requires strong relationships and finesse where women may actually have an advantage.