December 04, 2025
The Five Signals That Scream ‘Hire a Data Person Now’
“Should we hire a data scientist? Or a data engineer? Or both?”
This is the question every Series A biotech asks. Usually six months too late.
The Hiring Hesitation
I get it. Data roles are expensive. You’re not sure what you need. Your scientists can “just run Python scripts.”
But here’s what’s actually happening while you wait:
- Your senior scientist spends 15 hours/week on data wrangling instead of science
- The same analysis gets done three different ways by three different people
- New hires spend their first month just trying to find data
- Nobody can reproduce last quarter’s results
You’re not saving money. You’re just paying for it differently.
The Five Clear Signals
Signal 1: The Excel Limit Error
Scientists are hitting Excel’s 1,048,576 row limit. They’re splitting files, sampling data, or giving up on analyses entirely.
What this means: Your data volume exceeded desktop tools months ago.
What you need: Data engineer to build scalable pipelines + accessible database
Signal 2: The ‘Where Is That Data?’ Slack Thread
The same question appears weekly: “Does anyone have the data from that experiment we ran in August?”
Followed by: “Which version?” “The one Sarah sent?” “No, the corrected one.”
What this means: No data organization system. Knowledge lives in people’s heads and scattered drives.
What you need: Data steward or data engineer focused on infrastructure + governance
Signal 3: The Analysis Bottleneck Person
One person (usually your most senior scientist or a postdoc) is the bottleneck for all computational analysis. They have a backlog measured in weeks.
What this means: Analysis demand exceeds capacity. You’re slowing down science.
What you need: Bioinformatician or data scientist to distribute analysis load
Signal 4: The ‘I Don’t Trust These Numbers’ Meeting
Leadership meetings devolve into arguments about which dataset is correct. Different teams have different numbers for the same metric.
Nobody knows which is right. Trust in data is broken.
What this means: No single source of truth. No data validation. No accountability.
What you need: Data engineer to build proper infrastructure + data governance lead
Signal 5: The Duplicated Work Discovery
You discover that three different people independently built nearly identical analysis pipelines because they didn’t know the others existed.
What this means: No knowledge sharing infrastructure. Massive efficiency loss.
What you need: Bioinformatics lead to coordinate computational work
The Cost of Waiting
Real example: A Series A company delayed their first data hire for 8 months.
During those 8 months:
- Senior scientists spent ~600 combined hours on data wrangling (at $150/hr fully loaded = $90K)
- Three duplicated analyses (~200 hours of wasted work = $30K)
- One experiment had to be repeated because original data couldn’t be found ($50K direct cost)
Total cost of delay: ~$170K
The data engineer they eventually hired cost $180K/year. They broke even in their first year just from eliminating waste.
What to Hire First
Hire a data engineer first if:
- You have infrastructure problems (can’t find data, can’t scale analyses, manual everything)
- Your team is >20 people
- You have multiple data sources that need integration
- Scientists are spending >25% time on data wrangling
Hire a bioinformatician/data scientist first if:
- Infrastructure is okay-ish but you need specialized analysis
- You have analysis backlogs, not infrastructure chaos
- Your team is <15 people
- You need domain expertise in computational biology
Hire both if:
- You recognize multiple signals above
- You’re >30 people
- You’re planning to scale significantly in the next year
The Planning Question
You don’t need to hire immediately when you see Signal 1. But you should start planning.
When you see 2-3 signals simultaneously? Hire now.
When you see 4-5 signals? You’re already six months late.
The December Planning Opportunity
It’s early December. You’re planning 2026 headcount.
Ask yourself:
- How many of these signals are we seeing?
- What’s our current data chaos costing us?
- What could we accomplish if data wasn’t a bottleneck?
If the answers make you uncomfortable, add a data role to your Q1 hiring plan.
The Question
How many of these five signals are you seeing right now?
If it’s more than two, let’s talk about what that’s actually costing you.
