One of the best parts of working in life sciences recruiting is getting to spend time with the people who actually do the work. Every once in a while, one of those conversations sticks with you.
I recently sat down with Beth, a Senior Director of Data Management at a major CRO who has more than 25 years of experience in the industry. She started in data entry after college, never expecting it to become a career. So, naturally, 26 years later, here we are. Career planning at its finest.
“I didn’t think I would be here long, but 26-plus years later, here I am.”
Beth
Beth has seen data management move from largely paper-based processes to the technology-driven environment we know today. And through every wave of change, she has heard the same concern: will technology eliminate jobs?
Her experience says something different. The jobs didn’t disappear. They changed. Which is less dramatic than the robot-takeover version, but probably a lot closer to reality.
AI can speed up the work. It can’t replace judgment.
Naturally, we talked about AI, because apparently you aren’t allowed to have a business conversation in 2026 without mentioning it at least once.
Beth’s perspective was refreshingly practical. Her team is already using AI for documentation, plans, guidelines and other process-oriented work. But when it comes to applying AI directly to clinical data, she is more cautious.
There are real questions around privacy, security, compliance and accuracy. More importantly, clinical trials don’t always produce what we expect.
“The real data comes in and people are like, ‘Well, we didn’t expect that at all.’”
Beth
That point immediately made me think about recruiting. AI can screen resumes and identify keywords, but a resume doesn’t always tell the whole story of a person any more than one data point tells you the whole story of a trial.
I’ve seen plenty of candidates who can absolutely do the job get lost somewhere between the job description and the keyword filter. Context matters. Judgment matters. Knowing the person matters. Annoyingly for those of us hoping technology would solve everything, people still matter too.
Experience still sees what technology can miss
We also talked about the increasingly blurry line between data management and data science. And I should be clear: I am not a data manager. I have never been a data manager. No one should let me near a database unsupervised.
What I do have is years of conversations with people who work in the space. Beth’s reaction was basically that data managers already do a lot of what people now describe as data science.
They see trends. They spot gaps. They notice inconsistencies across datasets. She shared an example of recognizing that several patients’ data from one site looked too similar to be real.
“That wasn’t any computer that we ran this through. It was just, there it is. You can very clearly see it.”
Beth
No algorithm alerted her. Experience did. We can debate the titles all day – and in this industry, we probably will – but that kind of judgment is hard to replace.
The strongest takeaway had almost nothing to do with data
When I asked Beth what she looks for when building a team, she didn’t start with credentials or technical expertise. She started with honesty, accountability and being a good teammate.
“I can teach you how to do this job. I just need for you to be the team player, open and honest.”
Beth
At that point I told her I wanted to come work for her, which I’m sure my boss will be thrilled to read.
But as a recruiter, I wish more candidates could hear hiring managers say that. People put enormous pressure on themselves to have the perfect interview answer. Beth is looking for something more human: know what you’re good at, know where you need help, be willing to learn, and be someone others want on the team.
That same philosophy shapes how she leads through change. She has managed teams through mergers and integrations, and instead of pretending everything is great, she acknowledges when things are difficult and tells her team how they will get through it together.
In her most recent integration, which lasted roughly two years, she said her department was the only one that didn’t lose someone because of the integration. Turns out people respond pretty well when leaders don’t pretend chaos is a team-building exercise.
Q&A: Looking ahead
Steve Where do you see data management changing next?
Beth More patient-entered data, more connected devices and more information flowing electronically. She expects privacy and regulatory requirements to keep evolving, but she also sees technology changing how clinical data is collected and potentially reducing the dependence on traditional site-based collection for some information.
After talking about AI, data science, connected devices and the future of clinical trials, I kept coming back to one thing: Beth entered this industry when data entry really meant data entry. She watched the tools evolve, the titles change and the work transform – and she’s still here.
The technology will keep changing. The need for curiosity, judgment, honesty and strong human relationships won’t. From where I sit in recruiting, that may be the most important takeaway of all.
And if the robots disagree, Beth and I can revisit this in another 26 years.