It’s not you, it’s them: Larger companies take longer to call you back

tl;dr

I am about 30 days into a job search for a remote data science position, and I thought I’d report some evidence from the field. Increasing a company’s size by 10% will increase the number of days to set up a screening call by 3%.

Data Collection

I applied to 24 companies in this round, and at that moment in time I was shopping for a home among medium to larger teams. I kept track of my search in a spreadsheet, but here it is crudely put in a data.frame in in R.

Of these 24 companies, I was called to set up an interview from 10 of them.

#####
# R #
##### 
df <- data.frame(
  # company names
  company = c('Catalyst', 'Ascenscion', 'Aetna', 'Cigna', 'Clover', 'Genpact', 'CVS', 'IQVIA', 'Novant', 'Oscar', 'Mission', 'United', 'Medtronic', 'Siemens', 'Publicis', 'Dexcom', 'Oschner', 'Matrix', 'Rally', 'Invitae', 'GE', 'Renown', 'Virginia Premier', 'Atrium'),
  # employee population on linkedin
  size = c(700, 56194, 41439, 28388, 518, 112857, 68250, 45142, 10333, 836, 3527, 115727, 79230, 32491, 2818, 2370, 9208, 1149, 850, 720, 51990, 2990, 830, 4368),
  # number of days it took them to call me back, NA if no callback
  lag = c(8, NA, 8, 9, 2, 8, NA, NA, 3, NA, NA, 24, NA, NA, NA, NA, 7, 5, NA, NA, NA, NA, 1, NA),
  stringsAsFactors = FALSE
)

Company size — I quickly scanned the LinkedIn page to get the number of active employees on their platform. This is a reasonable estimate of the employee population. My intuition is that this estimate will be biased downwards for larger companies that have more diverse positions, but for the moment let’s take it as given.

Lag until callback — I measured the difference in days between my application date and my first point of contact with the company (e.g. ‘hello from X, we’d like to set up a phone screen). Note: this does not account for the number of days later the first call was actually scheduled.

Model Specification

In this particular application I am interested in the elasticity between company size and the lag until callback. To accomplish this I’ll use a transformation to build a ‘log-log’ regression model.

fit <- lm(
  log(lag) ~ log(size),
  data = df
)

I’ll quickly test to make sure this model specification is valid with the excellent gvlma package, or global validation of linear model assumptions.

summary(gvlma::gvlma(fit))

Remember that this is a log-log specification, and so the coefficient can be interpreted as an elasticity. In this case, for every 10% increase in company size we would expect a 3% increase in the number of days it takes them to call you back.

summary(fit)

With all the assumptions satisfied, we can now add the regression line and confidence intervals to our plot assess the fit visually. This seems reasonable enough, but I would caveat in this sample I don’t have any companies that fall in the range of 1000–8000 employees.

Do you have more data?

I would love to flesh out this analysis by increasing the sample size from a paltry 10 companies. Reach out via LinkedIn and tell me your experience so we can build a more robust regression together!