3 Sought-After Data Science Skills to Get Hired in 2019

Getting a job in data science is a wide-open prospect. Standing out and being successful is another story. Don’t languish in mediocre data science hell. Get to work on these essential skills to stand out in the data science field, get hired, and thrive, and find out what these skills all have in common.

[Related Article: How to Develop the Five Soft Skills That Will Make You a Great Analyst]

Data science job descriptions have a long list of required skills.  Inevitably various programming and modeling skills top the list. We’ve done some research and spoken with several hiring managers and here’s some of the most frequently mentioned skill that candidates often overlook

Here are 3 skills that will help you stand out in your current role or a new role in 2019.

Programming: SQL

Just over half of data scientist job postings specifically included SQL because the right query with real-world data has the potential to provide sweeping, exponential value to your CEO and business as a whole. Analytics is vital across industries, and only 58 percent of executives really believe they’re equipped to handle the data they acquire, with even more (68%) estimating they experience data blindness at least once a week. Data blindness means downtime, a prospect costing companies $700 billion annually. Reducing that downtime makes you valuable.

SQL targets these gaps with efficient search while other in-demand languages like Python and R round out the most desired languages in data science job postings, at 39 and 36 percent respectively. Both are high level, open source languages that allow you to build systems within a business that don’t just report data but help your CEO interpret what’s going on. Python can speed up those processes while R supports SQL and provides libraries for data manipulation and visualization.

As we know, the world is creating data at an unprecedented rate, currently estimated to be about 2.5 exabytes per day generated across about 8.4 million connected devices.  Much of that data is unstructured but for structured data, SQL is still the go-to data repository.  Additionally, many unstructured data stores are employing SQL or SQL like languages to access the data, so this skill has a long shelf life.

Communication: Data Visualization, Data Wrangling

Soft skills top the list of most all job descriptions, data science or not. You aren’t just dumping charts and graphs on the floor of your CEO’s office and walking away. You have to understand how to interpret the data your programs spit out, translate it into a working model or goal, and distill all that information into a slide for the next presentation.

Data wrangling (or munging) creates order from chaos. You don’t just return numbers; you return a functional set of outcomes or plans of action. You return the potential for business value. Anyone can get data one way or another, but the perk of a data scientist worth his or her salt is returning the right kind of data.

SQL and other programming languages and frameworks give you the power to clean and process that data so that your boss knows what to do with it. But even that may not be enough to set you apart. You also have to understand how to present it.

Data visualization returned just under 20,000 hits in a basic keyword search from Indeed, many clustered under the need for a data scientist. Businesses know they aren’t equipped to handle data, but plan to roll out machine learning initiatives anyway. Visualization clarifies the message distilled from data, a potential gap that could cost your business millions. Again, the dreaded downtime experienced when companies have data but don’t know what to do with it shrinks, making you potentially their most valuable employee.

Project Management: Agile and Scrum

Deploying data science at scale in complex production environments requires careful management of the many people, resources, and infrastructure needed to do so. No surprise but Project management, product intuition, and leadership skills are the newest wave of requirements.  Handing off raw models is not only a thing of the past, but so is segmenting the role of a data scientist to just pilot projects. If you’ve got the chops to see an entire project through from the very start of data discovery, and feature selection to all the way to the rollout of your newest service offering or product, you’ll be a whole lot more valuable as an employee.

Scrum, in particular, can be an instrumental project management side skill for the data scientist. It’s something many Fortune 500 and smaller companies are already familiar with, giving your project a head start. You can establish a context for your CEO and respective teams, set realistic milestones for both data and implementation, and address the data itself, particularly its quality related to the company’s current project.

PM puts you in middle of what’s going on, making you invaluable to your team as the data wrangler, visualizer, and interpreter. You can also effectively communicate any weak points regarding deliverables, turning those weaknesses into specific points of action.

Search results on Indeed.com with “data scientist” and “project management” or “scrum” returned less than 1000 results, but of those, every single one had a projected salary of well over six figures. While we know the salary doesn’t determine job satisfaction, it does indicate the rate at which your company is willing to invest in these two skills side by side.

[Related Article: Top Data Wrangling Skills Required for Data Scientists]

Business Value: The Real Deliverable

Working in data science isn’t about returning the numbers; it’s about the value you can bring to the company through targeted queries, curated data, and long-term involvement with your deliverables. Employers are looking for signs you can deliver this value for their company, and proving you’re not just a “numbers person,” but the full package, using programming, data visualization, and wrangling, and project management skills puts you square at the front of the line.