The Interview Query Secret Master Plan

Inspired by Elon Musk’s master plan for Tesla, I want to reveal the secret master plan for my company Interview Query.

It’s quite simple. We place as many data science candidates into jobs every week.

But there are two main challenges we have to overcome to achieve this goal.

One challenge is that we can only place 10 people into jobs if we sign up 10 users. So our mission is dependent on how fast we can grow.

Currently we can only be sure we get people jobs right now when they pay us for Interview Query premium and get access to all interview questions, courses, and learning materials on our platform.

We can lower prices to convince more users to use all of our platform. But we can’t bring it to $0 because the paying users fund our growth by making Interview Query better at shortening the amount of time between a user searching for a job and getting a job.

So at the end of the day, this business is going to get better through learning curves. By bringing more users on our platform, our job is to learn how to improve users data science skills faster and faster towards achieving their goal of getting their dream jobs!

This hypothesis is based on the fundamental assumption that more Interview Query usage correlates with a higher chance of getting a job.

I think this is logically correct. The more time you spend studying interview questions, the better you get at data science and passing your interviews.

I know for a fact that if anyone read every single question on the platform, they would be completely prepared for the interview. Because we source all of our interview questions from real interviews at top tech companies, our platform is taking the actual work from data science and engineering jobs, and scoping it down into a problem that takes an 30 minutes to an hour to solve.

The questions that these interviewers ask are real life scenarios that the interviewers have to tackle.

But here’s the second problem → data science interviewing and studying is taxing as hell.

Which means our second problem is more of a function of increasing user engagement in studying as many courses and questions as possible.

One way to do this is to feature our best and most engaging content at the very beginning of a user’s journey. If we assume that the equation of → Quality Data Science Content x Content Length = More Brainpower holds true, then our mission is essentially reduced towards a conversion funnel.

For example, if we convert 10% less users at sign up, then we’ve automatically converted 10% less users that have ever had a chance to engage with our courses.

If we don’t put our courses in the right order, then it’s 10% less likely that they’ll read the next one.

This reinforces the notion that all content on the site should be the same level of quality. The next value article the customer is reading should be just as good as the last thing they read.

And this can be done in another way.

A question recommender engine is an example of this. For every user, what is the absolute best question we can give you, that will convert you to trying just one more question. We can figure this out by understanding how difficult a problem is for any user, and level the next one accordingly.

Getting to this utopia is really difficult without accumulating data. But like other learning platforms we can start tracking it.

At the end of the day - our goal is to get people jobs. Getting jobs is a function of reading quality data science content, practicing it, and doing both back and forth until person is up-skilled enough.

Once a candidate is up-skilled - getting the job is trivial.

And that’s the Interview Query secret master plan.