The New Gold Standard for Career Switching
By: Shayaan Jagtap
Tags: Job Search, Job Hunting, Career Advancement, Careers, Data Science

Once upon a time, I was a bright-eyed freshman in college studying Business. Initially dead set on a career in investment banking, I slowly recognized that this was not the right path for me. I, like many reading this, recently came to terms with the daunting sentiment: “I need to switch career paths.”

As many early-to-mid career professionals in the same position often find, the next steps can be incredibly unclear. We must ask ourselves:

Which new career path is right for me?

What new skillset do I need?

How do I even acquire these skills?

The first question is very personal, and outside of the scope of this article. The next two, however, are what we will focus on answering. Today, I’m going to present a brand new approach to effectively identify and gather the skills you need to enter a new career path. To do so, this article will cover:

The Problems With Career Switching Today The Data-Driven Approach I’m Developing Address It How You Can Use It

Let’s get started.

The Problems With Career Switching Today

We live in a golden age of data science. With many industries experiencing rapid data-driven innovation, one remains suspiciously stagnant — the job market.

Career switching is no stranger to the average person.

Those of us without network connections in our target industries have limited options. Naturally, we turn to the internet, but the vast amount of information out there is a double-edged sword. If you search for guides on how to enter a new industry, you’ll quickly get overwhelmed; the internet offers less help than you’d think.

Why? Most of the information found online comes in the form of conflicting guides, each based on one person’s limited perspective. Anecdotal advice isn’t reliable, and even useful advice depreciates in value over time as industries evolve. A second option is going back to school (or a bootcamp), which can be time and cost prohibitive for most people. The last problem with both of these solutions is they assume one-size-fits-all. People don’t start from the same starting point — and that influences their optimal learning path.

In summary, today’s online resources are too subjective and de-personalized to be effective.

Because of these issues, many people get discouraged before starting, delay, or give up after sinking time and money trying.

So, how do we decrease this paralyzing uncertainty and increase our chances of success?

What I Built

As a data scientist & software developer, I get many questions on how to break into the industry. People ask me what they should learn, how to learn it, and what the fastest/cheapest path is. I didn’t know the answers, so I built something that does.

Let’s take a second to think about what a proper solution would look like. We’ll need a solution that is:

Data-driven: Hard numbers and industry aggregations, not anecdotes.

Hard numbers and industry aggregations, not anecdotes. Personalized: You start from a unique place and your path forward should reflect that.

You start from a unique place and your path forward should reflect that. Efficient: Getting you the best bang for your buck when building skills, no more wasted time and money.

Getting you the best bang for your buck when building skills, no more wasted time and money. Up-to-date: Industries evolve, so our solution must adapt to change.

Today, there is no solution that addresses all of these issues, but there’s hope.

From a lost business school student, I was able to successfully transition to a full time Software Engineer at Amazon. Now, not only have I experienced the issues with career switching, but I have the skills to help others make that transition. Let’s walk through what this solution looks like:

Step 1: Thinking like a data scientist, we know the best way to understand an industry is to look at the data. With enough data, trends will become apparent. Which skills are popular? Which are gaining popularity? How proficient do you need to be? With the right data, we could answer all of these questions.

Step 2: Now that we have an idea of the industry, let’s understand our job-seekers. Depending on their prior experience, their skillsets will vary widely. Some people might have half the skills required, others may have close to none. To capture this, we need to directly ask a job-seeker about their current skillset. The goal here is to collect information objectively to accurately gauge which relevant skills our job-seeker already possesses.

Step 3: Third, we figure out the skill gap. At this point, we know a person’s current skills and what’s required from their goal industry. Processing skills data into vectors and calculating these gaps would yield precise, meaningful numbers.

Step 4: Finally, we must close the skill gaps. The most reliable and widely available way to learn new skills is online courses. If we have access to courses from multiple big-name providers, we can provide an un-biased assortment of options for our job-seekers to get evaluated against. With a customized content-based recommender system, we can calculate an effective, personalized, actionable set of course recommendations for each candidate.