Career Roadmap: Machine Learning Scientist

To like machine learning engineers, machine learning scientists are in high demand in today’s job market. Indeed, organizations are eager to adopt machine learning-based tools to improve the value of their data and analytics and add automation to processes.

Amy Steier IDG

Amy Steier, senior machine learning scientist at developer tools provider, Gretel.ai.

According to market research, the demand for machine learning technologies is on the rise. Potential applications include customer segmentation and investment forecasting in the financial services industry; image analysis, drug discovery and personalized treatment in healthcare; and inventory planning and cross-channel marketing in retail. But machine learning can be used to improve processes in virtually any industry.

Naturally, there is a need for people who are experts in machine learning and related disciplines, and who understand how to use the technology for practical applications. Machine learning scientists certainly fit that description.

What a Machine Learning Scientist Does

Machine learning scientists share many of the same responsibilities as data scientists, including analyzing data and building models. Machine learning scientists also work closely with machine learning engineers. A machine learning scientist focuses on researching complex algorithms and building models. Machine learning engineers turn these models into products.

To find out what it takes to become a machine learning scientist, we spoke with Amy Steier, senior machine learning scientist at developer tools provider, Gretel.ai.

Become a Machine Learning Scientist

Steier earned a Bachelor of Science in Computer Science from the University of California, Santa Barbara (UCSB). She then earned a PhD in computer science from the University of California, San Diego (UCSD), with a focus on artificial intelligence (AI) and machine learning.

However, a career in technology was not a certainty during the college years. “Initially, I was a bit torn between psychology and computer science,” explains Steier. “But since I got into computers a bit more, I decided to specialize in this area. I enjoyed it immensely and never looked back.”

Mathematics had long interested Steier. “In my early school years, I was gifted and really liked math,” she says. “It was like a game for me. In high school, my teachers encouraged me to join the math club, so I ended up doing it. All my friends thought it was hysterical.

Steier began to grasp the idea that people must have an inherent tendency to appreciate what they are good at. “That belief was later a big motivating factor in my decision to go to graduate school,” she says. “I figured if I was going to dedicate so much of my life to my career, I should try to enjoy it as much as possible, and one way to do that was to get really good at something.”

During his graduate studies, Steier became passionate about data science and more specifically the power and potential of data. “Data science has always been a very fast-moving field, and to stay good at it requires constant learning,” she says. “My passion for the field constantly makes me want to learn and experience more.”

Early education and employment

After graduating from UCSB, Steier first worked as a programmer analyst at Computer Sciences Corp. (CSC) in 1986. At the time, the company was building a large financial system for the US Navy. “The work was satisfying, but I felt like I was learning a lot more about Navy finances than computers,” she says.

In an effort to hone her expertise and thus benefit more from her work, Steier went to graduate school in 1990. After exploring different subjects, she focused on the artificial intelligence group at UCSD and was able to work part-time at CSC for the first two years. .

Following this, Steier took a consultancy position with the Encyclopedia Britannica in 1992 and was able to use data from the Encyclopedia in her doctoral research. “The data they had was staggering in its richness and untapped potential,” she says. “Thus began my enduring and passionate love affair with data that will last my entire career. Its power, its mystery, its intrigue, its potential have always fascinated me.

After earning her doctorate, Steier became director of research and development, then vice president of research and development at the La Jolla Research Lab.

Follow his passion for data

In 2000, Steier took about a year and a half for the birth of her son. She eventually started again part-time as a consultant for ContentScan, doing smart literature review. From there, she took a part-time job in 2003 with Websense. She worked and eventually led the CTO office, exploring new technologies and product directions.

“At this point in my career, I was faced with an important decision,” Steier said. “Do I stay on a management path or reorient myself to focus more on practical work? I loved being able to set a vision for a band and help team members thrive in their careers. But I was passionate about manual work. I followed my passion and I have never regretted it. Even today, when I am asked for advice on the career path to follow, I always advise to follow your passion.

Steier took on a role at Websense as a principal researcher on a classification system for the web. “We primarily used large support vector machines to categorize content into over 80 topics and a dozen different languages,” she says. “This system is still in use today.”

When cybersecurity became a hot topic and Websense, eventually acquired by Raytheon and now called Forcepoint, spun off into a security company, Steier played a role in the cybersecurity group. “I got involved in a plethora of innovative projects focusing on both web and data security,” she says. “I worked on automatic malware classification, outbound malware detection, automated malicious website detection, threat landscape visualization and other innovative projects.”

In 2019, Steier had lunch with a former colleague who was on his second successful startup venture. “When he explained Gretel.ai’s mission and vision to me, I was immediately hooked,” she says. “The mission was to remove the privacy barrier to sharing data for everyone. Ease of access to data has been a thorn in my side for as long as I can remember.

Joining Gretel.ai “was like coming home,” Steier says. “My career has always been driven by my passion for data, and now I can focus on helping everyone unlock their power and potential.”

A day in the life of a machine learning scientist

“I like to start my workday by going through my reading queue and seeing what’s interesting or relevant to read that morning,” Steier says. “Then I usually have a few meetings a day, either on business or research team related topics. I try to cluster my meetings together so that I can devote time to whatever research project I’m currently on.

Sometimes this job involves more reading to explore what has been done so far or researching technological innovations that might inspire a new angle for a project. Steier spends a good portion of the day building various proofs of concept, each tied to a vision of the company’s product roadmap.

“We’re hiring right now, so once a week I’ll have a phone screen or an interview,” Steier says. “We write a lot of blogs, do interviews, do podcasts and talks, so I might spend time on one of these articles. Maybe once a month I’ll get involved with a specific company’s use case and help [plan] a solution. We chat a lot on Slack both on work-related topics and on random interesting or funny topics.

Career Highlights

We asked Steier about his most memorable career moments. “What really stands out is haha moment at the Encyclopedia Britannica, where I realized my deep love and fascination with data,” says Steier. “I remember the exact moment I was explaining it to a colleague at a conference. Saying it out loud really made me understand. I have carried this passion with me throughout my career.

More recently, “joining Gretel has re-energized me for my passion for data and what it enables in machine learning and AI spaces,” Steier says. “When I first started working in the world of data, much of what companies did was hampered by the inability to access or share data due to privacy concerns. But I observed that real-time change through synthetic data. Tools, like what we’re building at Gretel, are breaking down barriers and allowing data to become more and more democratized. I see this empowering tech communities around the world to use more of datasets and harness the power they provide.”

Getting a doctorate also opened a lot of doors, Steier says. “After that, continuous learning became a natural and necessary part of my career,” she says. “It’s always meant lots of reading, communicating with colleagues, and being open to trying new ideas.”

Inspirations and tips for others

Her parents were her biggest inspiration, Steier says. “For most of my life, my father was a professor of electrical engineering at USC [University of Southern California], and my mother owned several clothing stores. It was always clear that they loved their jobs. Going to college was never a question, just a natural part of growing up. Having the courage to push forward and go to college was solidly based on my parents’ unwavering faith that I could accomplish this.

“No life is without difficulties, but I believe that my passion in my work has helped me to be resilient,” she says. “Through each loss of a loved one, my work has provided a refuge that has helped me find my footing.”

For others looking for a path similar to hers, Steier’s advice is simple: “Educate yourself, follow your heart, and embrace lifelong learning,” she says.

Copyright © 2022 IDG Communications, Inc.

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