RESUME GUIDE
Consulting resumes love numbers.
Revenue increased 18%. Costs fell $2 million. Conversion doubled.
Then you look at your PhD.
No customers. No sales. No profit.
So what exactly are you supposed to quantify?
More than you think.
The mistake is assuming that every useful number has a dollar sign.
Your research may have created no revenue... but it still had scale
Suppose your bullet says:
Conducted electrophysiology experiments to study neuronal function.
Accurate.
Also almost impossible to evaluate.
Now imagine the same experience includes scale:
Designed and executed 200+ electrophysiology experiments across four treatment conditions, coordinating data collection and analysis over 18 months.
Nothing became "more commercial."
But the reader can suddenly see something.
Numbers make invisible work visible.
For PhD candidates, useful numbers often describe:
- volume
- time
- team size
- funding
- efficiency
- accuracy
- adoption
- audience
- scope
- improvement
The question changes from "How much money did I make?" to "What evidence shows how large, difficult, or consequential this was?"
For a deeper treatment of that translation, read How to Translate Academic Projects into Consulting-Relevant Experience.
But adding a number can still produce a weak bullet...
Consider this:
Analyzed 10,000 data points using Python.
Now it has a number.
Still not much impact.
Why?
Because 10,000 tells us the size of the dataset, not why your work mattered.
A stronger version might be:
Automated analysis of 10,000+ measurements, reducing a two-day manual workflow to three hours.
The number now helps explain the consequence.
| Weak number | What it proves | The sharper number |
|---|---|---|
| 5-year PhD | You spent time | Completed a major project 6 months ahead of plan |
| 3 papers | You published | Led 3 projects involving 12 collaborators |
| 20 students | You taught | Improved course process for 20 students |
| $100K grant | Funding existed | Secured $100K through a competitive proposal |
| 50 experiments | You did work | Increased experimental throughput 2x |
The number is not decoration.
It should change how the recruiter interprets the action.
What if there really is no obvious metric?
Then stop searching only inside the experiment.
Look around it.
Did you train someone?
How many people?
Did you improve a protocol?
How much time did it save?
Did you coordinate collaborators?
Across how many labs, departments, or institutions?
Did you present the work?
To how many people?
Did you win funding?
How much?
Did you build code or documentation someone else adopted?
How many users?
Your dissertation is not the only unit of analysis.
A PhD is surrounded by processes, people, resources, deadlines, decisions, and outputs.
Those are often easier to quantify than the science itself.
And sometimes the strongest bullet should not force a number
This is the reversal.
Not every line needs a metric.
A fabricated estimate, meaningless percentage, or suspiciously precise number can make a strong experience feel weaker.
Use numbers when they sharpen scale or outcome.
Then use language to sharpen ownership.
"Supported" feels different from "led."
"Helped develop" feels different from "built."
"Participated in" feels different from "launched."
The Resume Keywords: Consulting Action Words guide can help you tighten that first verb.
Then reread the bullet.
Does the reader know what you did?
Do they understand the scale?
Can they see what changed?
If yes, you have quantified the PhD.
Even if nobody bought anything.