MOUNTAIN VIEW, Calif., Aug. 27, 2019 /PRNewswire/ — Technical hiring platform HackerRank today launched HackerRank Projects for Data Science, an industry-first solution empowering recruiters and hiring managers to hire strong data scientists.
For the first time, enterprises can assess data science candidates on the specific skill sets they need to succeed—from statistics to data wrangling, machine learning, data visualization, modeling, and more—through real-world challenges in the Jupyter Notebook, the most widely used development environment built specifically for data scientists.
The demand for data scientists has grown 256% since 2013, but two important factors hinder businesses from filling these roles.
Firstly, data science is a field with cross-disciplinary skill requirements, so companies need to seek candidates beyond those with traditional software development backgrounds. Nearly 60% of data scientists learned their skills outside a university, and about 55% of them come from backgrounds including physics, mathematics, and biology. This diversity of background makes it difficult for hiring managers and recruiters to standardize the recruiting process and know which skills to prioritize in their testing.
Secondly, businesses are still learning how to identify the skills needed for these nascent roles. The terms data scientist, artificial intelligence engineer, and machine learning engineer are often used interchangeably, adding more confusion to defining the roles. This makes it challenging for employers to differentiate between distinct data science roles, all of which demand different skill sets. As a result, they miss out on the quality talent they need.
HackerRank Projects for Data Science solves both of these problems. The platform not only allows companies to evaluate a diverse talent pool against a standardized skill rubric, but also offers employers a real-world environment to test data science roles.
“With exponential growth in big data and companies using data to better serve customers across industries—from recommendations on your TV to autonomous driving in vehicles—the demand for skilled data scientists will continue to grow,” said Vivek Ravisankar, CEO and co-founder of HackerRank. “HackerRank has changed the way enterprises hire software developers—now, we’re bringing the same much-needed functionality to data science. This product will help close the gap between the expectations and needs of employers, allowing them to more easily identify and recruit the data science talent they need to ship innovative products.”
HackerRank Projects for Data Science enables enterprises of all sizes to:
- Assess advanced skills tailored specifically to data scientists. HackerRank Projects for Data Science assesses candidates on their proficiency in data wrangling, building models, machine learning, and data visualization, and offers specific skill rubrics for different data science roles.
- Standardize the data science hiring process. By predetermining the skills needed for a successful data scientist, companies can evaluate candidates across the same skill rubric, ensuring a consistent hiring bar.
- Simulate real-world job environments. HackerRank Projects for Data Science supports the Jupyter Notebook, the most widely used development environment built specifically for data scientists. This ensures a great candidate experience and an accurate showcase of candidate skills.
You can learn more about HackerRank Projects for Data Science here.
HackerRank is a technical hiring platform that helps businesses evaluate software developers based on skill. Over 1,500 customers across all industries, including 5 out of the leading 8 commercial banks in the U.S., rely on HackerRank’s automated skills assessments to evaluate and hire technical talent from around the world. Close to 6 million developers (over 20 percent of the global developer population) trust HackerRank to learn and practice coding. Every eight seconds, someone around the world completes a HackerRank assessment. For more information, visit www.hackerrank.com.