[gxs]

The crux of this problem in my opinion is that long term results aren't tracked.

It's hard enough internally to track someone's performance over the course of the year or two after they get hired, it would be even harder to do it if you are a recruiting company.

It's especially sensitive because employers are weary of sharing employee performance data to third parties because of the high risk of a lawsuit (there is clear precedent for these lawsuits.)

Once that data problem is bridged, it blows the problem right open for data to be explored and figure out what exactly predicts a top performer, in any field.

[Benjammer]

>Once that data problem is bridged, it blows the problem right open for data to be explored and figure out what exactly predicts a top performer, in any field.

Yes, assuming there is some top-level "data problem" to actually bridge here...

How do we know that the concept of "top performer" isn't just a completely divergent idea that means different things to different people and different companies in different industries and different geographic areas?

[metaobject]

I'm inclined to believe that measuring performance is fairly subjective. However, in an attempt to put some numbers behind a performance score, you might have some combination of individual weighted scores that involve things like:

(1) length of time employed at the company

(2) some measure of the performance feedback the engineer receives in their annual review - which may include percentage salary increase (possibly subjective, though)

(3) a score compiled by surveying the employees' peers (again, possibly subjective)

(4) the overall TripleByte turnover rate at said company

(5) the market average for any/all of (1)-(4)

[gxs]

Sure, it's a subjective measure, but it's a start.

Keep in mind there is ZERO data available for this type of analysis at the moment.

Even if it's subjective, the analysis can provide the employer with candidates that are good based on the same subjective criteria.