Data Science: The Most In-Demand Skill on the Job Market

Does working with massive databases and connecting numbers to narratives sound rewarding? How about money, opportunity, and job satisfaction? Evidenced by HR surveys and CyberCoders job postings for six-figure salaries for data scientist candidates, the career is peaking at the moment.

How to grasp the fluid meaning of data science

Technically a data scientist may have several titles. Senior management might be called the head of predictive modeling and analytics, heading up a team of data scientists. Other times you might be the one-person business analyst working internally. In both cases you extract conclusions by sorting raw data; checking its integrity; applying metrics and testing hypotheses in sales situations. The end deliverable models findings with the goal of improving business operations.

Careers in data have three tiers each demanding a different skillset

Companies hire out the ‘grunt work’ to offshore KPO (knowledge process outsourcing) firms for the collection and entry of information to grow databases into big data. KPO data miners scour IP documents, earnings reports, R&D documentation, and conduct market research for client companies. They facilitate a swift and purposeful go-to-market for a product or service.

Experts in machine learning make data readings timely and actionable by programming correlations into unstructured data to create logical outputs. Typically machine learning is coded in languages like R or Python. Fluency in the languages helps, but understanding how data connects to business objectives separates mid-and top-level candidates.

The best in the field know what value looks like in their industry, and possess the tools for gathering and developing data that matters. Those working in fintech will have CS and accounting practices; if you’re in pharmaceuticals you will have CS with organic chemistry, and so forth. The combination allows for creative thinking in generating models that yield real business value.

Top candidates have a combination of rare skills 

If you earned a BA in data analysis you’ll likely qualify for KPO work. Advancement down the career path requires a rare combination of hard science and soft skills. Project management across several teams, interview-based information gathering, and explaining technical concepts to less technical stakeholders demand another set of chops entirely from data preparation.

A more sensible route into the career starts with a science or business administration focus at the university level followed by post-grad (or even bootcamp) work in programming for data science.

Everyone wants refined, actionable data

In the five years that data science appeared in the business vernacular, the mainstream corporate establishment has fully adopted its practice into business frameworks. Before recently, data science fit best at small companies due to the freedom to start with a blank slate using a new technology and mode of thought.

That has changed somewhat, though not entirely. Fortune 500 companies lured experienced data professionals away from smaller firms, but older companies have a hard time finding young talent. Part of it is that younger data scientists seek out startups to gain entry level experience while having the freedom to experiment and set their own processes. A junior position at a large company where they are forced to adhere to processes established by senior staff is less attractive.

Established companies using decades old recruiting methods turn off young talent. When you’re a young professional in-demand and eager to work, a weeks-long multi-interview process stains patience. Especially when candidates know their own data is out there in plain view.