Despite a lot of opportunities that various industries have for data science positions, talent recruitment has become one of the most daunting tasks to recruit the right talent. For instance, hiring for data science roles cannot be done without understanding the skills, tools and technical knowledge that they actually pose. Some of the basic requirements by data scientists are a thorough understanding of tools such as Excel, Tableau, Python, Java or Scala, Big Data knowledge from platforms like Hadoop or Spark. It also requires the candidate to be able to take responsibilities such as data acquisition, collection, data cleaning, and more.
Not just the theoretical but practical understanding of the tools and to be able to build solutions and real use cases matter the most. Also, the fact that many professionals like to call themselves data scientists without having the right credentials is another challenge.
While companies have been hiring the traditional way through job portals, that typically includes getting applications, pre-screening, technical test, personal interview and selection, companies are now opting for other non-traditional ways to hire data scientists.
In this article, we list down 5 non-traditional hiring methods that are largely being adopted to hire data scientists:
Hackathons: Hackathons have become one of the most liked methods in the analytics community to hire the right talent. Many companies, big and small are resorting to partnering with hackathon platforms to spot the right talent. It is one of the most efficient ways of hiring professionals in the areas of machine learning, AI and data science, especially for the talent-starved industry. It is much more efficient as it allows the participants to work on real-life business scenarios and demonstrate some real problem-solving skills. Even if you are a beginner with the potential to get into the industry, this is one of the best mediums to get hired.
Participate in our currently on-going hackathon here.
Github: Github acts as a perfect platform to showcase work by machine learning and data science enthusiasts. It serves as a popular site to hosts code repositories, data, and interactive explorations, present your work and impress hiring managers. The job aspirants can easily set up a account on Github to create a repository of your work. Github allows collaborating with team members while acting as an online resume to showcase coding skills. It is acting as a revolutionary platform to analyse data scientists with potential skills.
Conferences: We have earlier covered articles on how events such as Cypher and Machine Learning Developers Summit is the most ideal platform for hiring data scientists. Conferences give an excellent platform to learn from, but also present with a host of networking opportunities. As conferences host a lot of delegates from various walks of data science and analytics industry, it always is a good opportunity to learn about new job openings in the field. Setting up stalls in developer summits can also prove to be quite effective for data science hiring needs.
Whatsapp Communities And Groups: Whatsapp groups are now turning out to be the most unconventional way of recruiting data scientists. It allows for a free-wheeling conversation between job applicants and recruiters. There are many WhatsApp groups that are currently bringing the data science community up and closer, most of which are invite only. It gives exposure to the who’s who of the analytics ecosystem to not only discuss the ideas and innovations in data science but posting and making the most of job opportunities in the field.
You can join MLDS WhatsApp group with this link.
Meetups: There are many meetups groups that are acting as job portals to look for the right job openings in the field. It can be a self-organised gathering of people or organised by companies with specific recruiting needs. It is an effective way to connect and meet in-person with the people in data science and analytics industry.
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Srishti currently works as Associate Editor for Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.