Harvard Business Review once called the role of a data scientist the “sexiest job of the 21st Century.”
While we’ll leave that for the experts to decide, there’s no disputing the demand for talented data scientists in the nations workforce. According to federal employment data analyzed by CompTIA, an IT trade association, US companies had around 918,000 unfilled IT jobs in the past three months.
Every day companies are looking for skilled data scientists to join their teams. Check out all the companies on Ladders that are hiring data scientists in particular, including big names like IBM, Lyft, and Verizon.
Demand for data scientists begs the question: what exactly does a data scientist do? Ladders spoke with experts from cnvrg.io and SEMRush to find out all about the role of a data scientist, from the interview process to the day-to-day aspects of the role.
What does a data scientist do?
“A data scientist is often a part of a data science department that provides services to other departments in the organization and solves complex problems using data,” said Yochay Ettun, CEO of cnvrg.io.
“In some cases, data scientists are doing both science and engineering, depending on the support resources your company has, whether it be an MLOps solution, ML engineers or data engineers on your team.”
Examples of data science jobs
Micron Technology is currently hiring for a Data Scientist to work in Boise, Indiana. The Walt Disney Company and Macys are both hiring for a Data Scientist to work in New York, New York. IBM is hiring for an open Data Scientist role in San Fransisco, California.
How much do data scientists get paid?
The salary range for data scientist jobs on Ladders is $90,000 to $225,000.
What do data scientists study in college?
“Data scientists are typically researchers and often require advanced training in either computer science, another scientific field, or a mathematical field,” according to Ettun.
“Data scientists typically require skills in programming, computational fluency, advanced math ability, scientific methodology and even soft skills like communication and collaboration. A data science candidate that has all of these skillsets is often called a unicorn, as there is a shortage of talent that has all of these skill sets. Luckily new machine learning technologies and development platforms have made it easier for data scientists to perform much of the computational functions with MLOps. This allows data scientists the ability to focus more on building algorithms or machine learning models.”
What kinds of projects does a data scientists usually work on?
According to Ettun, data scientists in enterprises are responsible for building machine learning models that will bring business impact to their company.
“Depending on your industry, there are plenty of different machine learning projects that data science teams could be working on,”Ettun said.
“For instance, computer vision to identify diseases in Healthcare, Churn prediction in Gaming, Fraud Detection in Banks, crop yield prediction for Agriculture, Anomaly detection and more. The opportunities for machine learning in any industry are endless. The data science workflow of creating those machine learning applications often starts with understanding a business problem, understanding and preparing the data, training, and experimentation of models, evaluating, and eventually deploying the model to production.”
What do hiring managers and recruiters look for when hiring a data scientist?
Hiring managers are on the look out for several different skill sets when adding new data scientists to their team.
“On the one hand, they look for skills that should apply to every data scientist such as knowledge of analytical tools, fluency in Python coding along with Java, Perl, or C/C++ and R(programming language), machine learning mechanisms and statistics,” said Maxim Roslyakov, the Senior Vice President of marketing at SEMrush.
“On the other, they are looking for skills that are normally considered outside the box as data scientists’ role is more than just proving numbers depending on the industry. For example, here at SEMrush our data scientists are teamed up with the media teams to create topical and newsworthy content,” Roslyakov said.
“Be it from global search trends to breaking news – together they use their knowledge and skills to provide urgent data reports that create headlines across national news organizations be it television, print, radio or digital. That’s why data visualization skills are equally important for them along with the ability to analyze large sets of data in an instant.”
What kind of experience is important for a data scientist?
“It’s inherent to have previous data science experience whilst working at IT companies,” Roslyakov said. “We also take into consideration if a person has participated in Hackathons or similar collaborative software projects. In regards to data scientists’ cooperation with media relations team, if the person is also aware about the current media agenda — that’s a big advantage.”
What does the interview process typically look like for a data scientist?
According to Roslyakov, the hiring process for a data scientist role typically includes quite a number of steps.
Roslyakov outlined the interview process steps for Ladders:
- First, the recruiter finds the candidate and organizes an initial phone call with a hiring manager.
- If the manager considers the candidate to be fitting, the next step is a personal meeting where the team can ask the candidate any questions and analyze the potential of a successful work relationship.
- The next process is reviewing their working skills — the candidate is sent a test task which they are required to fulfill by a particular deadline.
- If they are successful in that, the final part is a call or a meeting with the head of a corresponding department from the company to provide an official job placement.