Nope, there's many other data science shops in McK, although the branding is beginning to merge under the QB umbrella.
I see little difference. Sometimes it's an EM + 1 or 2 data scientists. Sometimes its an EM + 1 data scientist + 1 or 2 BA/Associates. We solve similar problems, its just the data science people tend to solve it with data and the traditional consultants solve it with interviews and whatever. That's not a swipe at them either. There's a significant quantity of problems that can be easily uncovered by talking to someone who knows the answer but can't get the business to acknowledge it OR by spending a week churning through data to discover it yourself. You might feel better with the hard fact base in hand, but doing those interviews and using the traditional consultant skill set tends to make you far more targeted and efficient in scoping and planning.
"QB" I think operates a little differently, whereby they get the data and tend to do more stuff on their own with dedicated modelers, data engineers, and designers. More "I am the expert here, leave me be". (Also not intended to sound like a swipe. That's a reasonable point of view sometimes).
This only really applies to really large companies mind you, but I would say:
1) You don't need to work your way up the approval chain. Your idea is already going to people with the authority to act on it.
2) You have significant ability to just call people and get them to share their data and domain knowledge
3) You have little risk of long term problems from saying something that someone doesn't like, so long as its not the person who hired you.
4) The pace is probably very very fast relative to the speed of most large orgs. Probably not sustainably fast tbh. It's fine because it's several weeks of hard focus and then a bit of a break.
If you feel like you're doing fine and are answering important questions then you probably are doing just fine and your company's data culture is healthy. IMO people who build analyses that few people want or need are acutely aware of it.
No... that's not a good explanation. While it is true they're salaried, there are plenty of professions that bill by the hour for the time of salaried employees. How you bill doesn't need to match how you pay salaries. It could also be billed per day of work regardless of the number of hours and the issues mentioned above would still be pertinent.
I would take a different perspective. The issue is not 23 year olds being better than others. It's the fact that the 23 year old from McKinsey comes in with incredible political capital equivalent to whoever hired him at the top, and that enables him to be multiple times more productive than someone else. Most organizations are... slow.
McKinsey data scientist (basically a consultant who codes). Can answer this after following some colleagues who have left and seen many technical teams at clients for my general role.
Most data scientists... don't know what to do. And most clients... don't know what to do with them. So they do little data science projects and build dashboards that few people use. Coming out of McKinsey you can quite confidently say you've learned how to identify top problems, organize a project around it, and solve it in a fast timeline with realistic solutions. That's pretty much gold. It's really useful to be able to say you've worked with c-suite leaders.
Finally, it's probably a function of just fast growth internally. You gain responsibilities much faster at McKinsey than at most most companies. The company invests in your development more. And when you leave, you get weeks of paid time off to search for a new opportunity.
I see little difference. Sometimes it's an EM + 1 or 2 data scientists. Sometimes its an EM + 1 data scientist + 1 or 2 BA/Associates. We solve similar problems, its just the data science people tend to solve it with data and the traditional consultants solve it with interviews and whatever. That's not a swipe at them either. There's a significant quantity of problems that can be easily uncovered by talking to someone who knows the answer but can't get the business to acknowledge it OR by spending a week churning through data to discover it yourself. You might feel better with the hard fact base in hand, but doing those interviews and using the traditional consultant skill set tends to make you far more targeted and efficient in scoping and planning.
"QB" I think operates a little differently, whereby they get the data and tend to do more stuff on their own with dedicated modelers, data engineers, and designers. More "I am the expert here, leave me be". (Also not intended to sound like a swipe. That's a reasonable point of view sometimes).