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Data Chat: Ellen Friedman

We are proud to say Ellen Friedman has been a part of Big Data LDN since the very first year. She is our fifth inductee into the Big Data London Hall of Fame. Read about her career journey and how she moved from molecular biology to machine learning and analytics.

 

What’s your data origin story?

 

I began my career as a biochemistry and molecular biology research scientist, first at Rice University and then at the University of California in San Diego (UCSD). My doctorate is in biochemistry, but my research was mainly in molecular biology, and I also taught part-time at their medical school. Then I moved to New Mexico State University where I worked at a plant genetic engineering laboratory focusing on new crops for arid and dry agriculture. I did that for quite some years.

 

After that, I worked on government-supported research and education projects using the latest research in genetics to write national-level textbooks or build new curricula for high school, college and university education. Then I began working independently as a consultant, writing educational video scripts and storyboards. Finally, I began to write not only about the genetics and biochemistry I trained in but also about broader technical topics.

 

One of the most interesting projects was working for the US agencies NOAA and NASA. They used what was then a new programme, remote-sensing satellite data, to study oceanic, atmospheric and land science on a global scale. It seems obvious now, but it was absolutely a new thing then. Initially, they hired me to interview twelve of their scientists worldwide and translate their technical research into a form that the public could consume. I had to break down specialised research and transform it so anybody, with or without training, could get to the heart of what mattered.

 

About twelve years ago, I pivoted into large-scale data and machine learning. I was invited to co-author a technical book about using the first large-scale, open-source machine learning library, which was part of the Apache Software Foundation. The software is called Apache Mahout, and our book title was Mahout in Action. The book got a lot of attention, and suddenly there were many requests for me to write or communicate about other aspects of machine learning and large-scale analytics.

 

I never made a conscious decision to move away from molecular biology and genetics, but somehow, I never looked back.

 

What advice would you give to someone looking to pursue a career in data?

 

I’m fascinated by information and data. But it’s all data. That’s the common thread that makes my career look so different at different stages when, really, it’s all the same. And I have a genuine desire for serious learning. It doesn’t matter what the form is; I simply want to use my ability to think. My father would say, ‘You have a good brain, so use it.’ Of course, your path might be smoother if you do more planning than I did, but when opportunities came along, I reacted to them, and that’s taken me down an exciting road. So my advice is:  be open to opportunities.

 

What’s your biggest success?

 

Not so much my accomplishments, but a couple of things stand out in my career.

 

The first was when I had just finished my PhD and moved to California. In those early days of molecular biology, we were still working out how genes were turned on and off. Initially, this was studied in bacteria, and now people were curious about how those switches worked in other organisms. I had just started, as a postdoc at UCSD, and I saw a notice saying “if you are interested in intervening sequences, bring your lunch to [this room]”. Intervening sequences are little pieces of DNA inserted into the code for a particular gene; in modern terms, they’re called Introns. They had just been discovered, the work hadn’t even been published yet, and I had learned about it because as a graduate student at Rice, I had worked across the street from where some of this work was being done. So I showed up, and there were about twenty-five people there, mostly from my department. It was a completely informal conversation, but Nobel Laureate Francis Crick led it. That moment was crystal.

 

The other one was two years ago when I was invited to do a Keynote at the JFokus developer conference in Stockholm. People who have traditional developer skills are becoming increasingly interested in data science, and this conference had the biggest in-person live audience—2000 people in a room and another 1,400 watching via live link – of any I’ve spoken to. It was so exciting, and the audience was hugely enthusiastic; they were excited about doing something new, learning something new. It hit me how cool that was as I walked on stage.

 

Don’t miss out on your opportunity to learn from Ellen and over 180 other expert speakers at this year’s #BigDataLDN. Register free now!

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