Many companies are now harnessing the power of data by hiring top data science talent. You’ll find data scientists in all sorts of industries – government, tech, manufacturing, marketing, and so much more! It’s an in-demand job for good reason. A world that has become more digital requires the expertise of data scientists to analyze data to create data-driven decisions. While “data science” has been a buzzword for quite some time now, one cannot deny the importance of recruiting the best data scientists for your company.
What Are The Secrets To Finding And Hiring Top Data Science Talent?
With emerging fields such as data science, you’ll need all the best talent you can get – considering that companies of all sizes are looking for the best data science professionals. Many organizations are constantly finding new ways to attract new hires, but there are other secrets about this process you still haven’t heard of. If you’ve been struggling with hiring top data scientists, here’s how you can improve your recruitment process.
Write Complete Job Descriptions
If there’s one thing new hires look for, it would be a complete job description. That’s why this is the first thing you must pay attention to when looking for new talent. Keep the job description at 500-600 words to keep it substantive while maintaining the prospect’s attention. When writing a description, it’s crucial that you provide a summary of the role together with your expectations of the candidate. Explain what makes this role unique to your organization.
Take note that there are other things you must consider when crafting the perfect job description. Here’s another tip: writing jargon is a big no-no. It’s easy for candidates to be swayed to the other direction when they skim descriptions with complex terminologies that are difficult to understand. In short, just keep it simple. It’s also worth noting that if you want to keep prospects interested, it’s a must to include the benefits of working with your company. It’s simply one of the best ways to keep them interested – enumerate the perks of working with you so you can keep their interest. As a bonus, try looking at the job postings of your competitors and see what sets yourself apart. Then, use this information to your advantage.
Use Niche Job Boards
Many recruiters use well-known job boards when looking for data scientists such as LinkedIn, Indeed, and Glassdoor. While these job boards certainly help in the hiring process, you can consider using niche job boards for data science instead. Dice, for example, is one of the job boards you can use when looking for talent in the data science industry. You can search for new talent all the way from entry-level to senior positions. Other popular tech job boards you can look into include AngelList and Hired.
Search For Candidates With The Right Skills
Of course, there’s a list of soft skills that you must consider as well. Communication, problem-solving, time management, and critical thinking are some soft skills that you must consider before hiring talent. These might be difficult to assess if you haven’t had an interview with the candidate yet, but it’s important to tailor your questions to find out if they have the soft skills necessary for a job in data science.
Review Your Interview Structure
Here comes another important aspect to consider before recruiting – your interview process. The interview is the best way to be acquainted with a candidate’s skills and behaviour. The process usually starts with a pre-screening phone interview which is followed by a panel of online or in-person interviews. In these interviews, you must ask all kinds of questions: structural, behavioural, and technical. That way, you can make an informed decision of hiring your candidate. For technical knowledge, you can even use testing platforms such as MachineHack and DataCamp to determine if they have what it takes.
In your interview panel, it’s wise to vary the employees they meet. Allowing different employees to be part of your panel provides you with different viewpoints that you might not have considered when hiring. The panel must be informed about the skills that you are looking for and what you’d like to know more about the candidates. It also helps to interview in pairs or groups to prevent interviews from going astray.
Treat Candidates Like Customers
One thing companies overlook is their treatment of prospects. Remember, the hiring process goes both ways. While you would be interested in attracting new talent, these candidates would also determine if your company is an excellent fit for them. There’s no better way than showing your company is right for them by treating them as if they were customers in all stages of the hiring process. In a field as competitive as data science, it’s important that your company stands out from the rest of the pack. You can achieve this by promptly making follow-ups and informing them of the outcome of their application. And when things go south in the hiring process, you leave a bad taste in the mouth of these candidates. Word can spread quickly about your company’s reputation – preventing you from attracting new talent in the future.
The field of data science is inevitably gaining traction, making your search for exceptional talent all the more difficult. Attracting the best talent should be a priority of any company in order to achieve its goals. After all, a company’s greatest asset is its people.
About the Author
Bash Sarmiento is a writer and an educator from Manila. He writes laconic pieces in the education, lifestyle and health realms. His academic background and extensive experience in teaching, textbook evaluation, business management and travelling are translated in his works.
LinkedIn – Facebook – Instagram
If you liked this article, maybe you will like these too.
5 reasons why you should do customer segmentation.
How is machine learning used in Cybersecurity?
Can data science be automated?
How is Data Science used in Influencer Marketing?
How to calculate the Customer Retention Rate on Shopify using Python?
Can machine learning detect Fake News?
Machine Learning project for Beginners