Career Advice for Data Science and AI Professionals at Every Stage
At COLLIDE 2023 in Atlanta, Burtch Works' Beverly Wright and Crunchbase's Jess Ramos shared career advice for data science and AI professionals at three stages. Topics included choosing a good manager, ethics, soft skills, specializing and mentoring.
By The AI Enterprise Magazine Team4 min read
Key takeaways
- Pick the manager, not just the title. Ramos said early-career professionals grow most with a supportive mentor-like manager.
- Mid-career is not a ladder. Wright described careers as a jungle gym, and Ramos said staying an individual contributor is a valid choice.
- Expect ethical pressure. Wright said she had been asked at least ten times to make numbers justify a decision already made.
- Soft skills and specialization open doors. Stakeholder management and a niche by problem, technique or industry help, within limits.
- Later career is about fit and giving back. Confirm you love the work, take stock, and mentor the next generation.
At COLLIDE 2023 in Atlanta, Beverly Wright, Head of Data Science and AI Solutions at Burtch Works, and Jess Ramos, Senior Data Analyst at Crunchbase, gave career advice for data science and AI professionals at three stages: early, mid and later career. The 21-minute conversation drew on both speakers' experience, and they argued the guidance has to differ by stage because both the field and the person change over time.
Wright said she also serves as president of the Analytics Society at INFORMS and chair of the data science and AI society at the Technology Association of Georgia, and hosts a podcast. Ramos, who described a day job in analytics and a second life as a content creator, also teaches data analytics and SQL courses on LinkedIn Learning. The two met through an organic mentorship that grew out of a volunteer data science for good project, Wright said.
What advice do experts give early-career data professionals?
Ramos put the manager first. A good manager, she said, gives a new analyst room to ask questions and grow technical skills and confidence. In her first analytics job, she said, she went from selecting one column in SQL to building data models.
She cautioned against fixating on prestige. "What's most important is being able to have someone who's going to nurture you and allow you to grow," she said, because those skills travel to the next job. Asked to choose between a great job with a poor manager and the reverse, she said she would lean toward where she would grow the most.
Her second point was impostor syndrome. She said she still feels it, and that "everybody brings something valuable to the table" regardless of background or years of experience.
How should mid-career professionals plan the climb?
Wright recommended writing a personal mission statement and keeping it somewhere handy, such as a phone's notes app. Hers has four parts: elevating data science, stimulating innovation, encouraging collaboration and doing as much good as possible. For people stuck, she suggested imagining what colleagues would say about you at a retirement party.
Ramos advised against feeling pressured onto the manager track. She said she managed a team of two at a startup, concluded she needed to develop technically as an individual contributor, and moved to a senior role she sees as "a step in a different direction." Wright agreed and described a career as "more like a jungle gym than a ladder."
Why do ethics and soft skills matter in data careers?
Wright said ethical challenges arrive in mid-career, when work starts to have impact. She recounted a colleague at a major Atlanta company who refused a stakeholder's demand that the numbers be made to look a certain way. Wright said she had faced similar requests at least ten times, "to justify a decision that's already been made instead of relying on the data."
Ramos said soft skills, not just Python and machine learning, move people to the next level: managing stakeholders, knowing when to push back, building trust in metrics, scoping projects and mentoring others. Wright said that when Burtch Works hears from employers, they ask for people who are autonomous, proactive and naturally curious.
Should data scientists specialize?
Wright said not to be afraid of it. Specializing, she argued, increases opportunities because people become known for something. She named three routes: by business problem, such as consumer loyalty; by technique, such as natural language processing; or by industry, such as health care or financial services. She added a limit: do not get so granular that you cannot get another job.
What should later-career professionals do?
Wright, who said she has worked in the field for 32 years, urged senior people to confirm they are doing what they love. She said two executives she mentors told her they climbed the ladder because they could, and ended up unhappy. She also recommended taking stock of a career, asking what is missing before retirement, and mentoring: speaking at universities and giving back to the next generation.
Ramos closed with advice for everyone: chase what excites you and stay adaptable. Wright added: stay curious.
Where to hear more from data and AI leaders
COLLIDE 2027, billed as the largest AI conference in the Southern U.S. with more than 1,000 enterprise data and AI leaders, takes place Wednesday, October 13, 2027, at the Sandy Springs Performing Arts Center in Atlanta. Details are on the COLLIDE event page, and registration is coming soon. For ongoing conversations, AI Connect also lists 24 free live webinars on the 2027 schedule.
Frequently asked questions
What career advice is there for early-career data scientists?
Ramos advised prioritizing a supportive manager who helps you grow over title or salary, and working on impostor syndrome by remembering that everyone brings value.
Do you have to become a manager in data science?
No. Ramos said people should not feel pressured onto the manager track, and Wright compared a career to a jungle gym rather than a ladder.
Should data scientists specialize?
Wright said yes, by business problem, technique or industry, but not so narrowly that it becomes hard to find another job.
What soft skills matter for data professionals?
Ramos listed managing stakeholders, knowing when to push back, building trust in metrics, scoping projects and mentoring others.
This article is based on remarks by Beverly Wright, Head - Data Science & AI Solutions, Burtch Works; Jess Ramos, Sr. Data Analyst, Crunchbase at COLLIDE Data + AI Conference 2023 on October 3, 2023. More coverage: COLLIDE AI Conference.
Filed under: People, Culture & Leadership
