Data Scientist Job Description Template (2023)
Understanding the Data Scientist Job Description
You don't need technical skills to write a data scientist job description. Hiring expert data scientists simply requires a thorough job description to communicate your expectations and cultivate interest in working for your organization. Learn how to write the best job description in 2023!
Your job description needs to stand out to attract data scientists. Demand continues to rise rapidly. Coming into 2022, demand for data science experts had skyrocketed 295%! According to the U.S. Bureau of Labor Statistics, the job outlook for data scientists is set to grow "much faster than average", at 36%. In Canada, future job prospects for data scientists are high across the country.
Creating a job description can be intimidating, so we'll start with a concrete example to demystify the elements of a good job description and identify the elements you'll need to customize. You can use our sample data scientist job description as a place to start, or as inspiration to develop your own. Our template is the best way to hire data scientists!
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The Data Scientist Job Description: A Detailed Example
A detailed example of a data scientist job description can be found below. We've filled in the details for illustrative purposes: be sure to tailor the details to the needs of your organization.
The specifics we've included target a "vanilla" data scientist with a few years of experience. Adjust the expectations, responsibilities, and skills if you intend to hire senior data scientists, staff data scientists, or data science managers.
You will need to consult your HR and legal departments to ensure compliance within your organization and within the laws of your geographic region.
Data Scientist Job Description Sections
Each of the sections are equally important. Use the details below to take a deeper dive and tailor the template to your organization.
Start your job description with key information. Candidates need this to determine if your role is a viable fit. It will save you time if incompatible candidates filter themselves out. Common details to include are the posting date, the type of role (full-time, part-time, or contract), where the employees are able to work (including hybrid locations), and salary ranges. For more details on data scientist compensation and salary levels in Canada, check out our salary guide.
Details about the role are critical and serves two purposes. The first purpose is to summarize what the job will entail. The second purpose is to get applicants excited about working for you. Enticing candidates with the role description will increase your number of applicants, and with the right pitch, can entice overqualitifed candidates to take a pay cut to work for you. Include who new employees will report to: this is a signal for the level of responsibility of the role.
Think about what makes your organization unique. For example, if the position is with a large organization, the sheer volume of data may mean there are lots of interesting opportunities to analyze it. For a smaller organization, candidates may find it exciting to be involved in laying the foundation of how your team collects and uses data to grow.
Data scientist job responsibilities are important to communicate to prospective clients, as different companies have different expectations. Describe what your ideal candidate would accomplish working for you. You'll want to cover the core day to day responsibilites, the expected products of their work, and how you want them to interface with the team.
This section is most valuable to candidates when it is concisely presented, and thoroughly captures the expectations of the role.
The required skills section is another opportunity for candidates to understand if they would be a good fit. This can be a chance for poor candidate matches to filter themselves out of the process, and can be an opportunity to showcase any interesting technologies that your team has available.
Capture required qualifications in a list.
Once you've identified data scientist skills and responsibilities, you can include information about why your company is an appealing place to work.
Showcase your company's benefits and perks. Include necessities, like vacation time. Prospective employees will want to see if the intangible value of working for your organization matches their needs and hopes for work-life balance.
Creative benefits can also set your company apart. For example, famously, companies like Google and Facebook have offered a free food benefit. When compared with the yearly salary of tech employees, the allure of free food is a relatively cheap way to entice and retain staff.
Providing benefits that support your company's identity can be effective. For example, if your company works in the health space, providing benefits relating to a exercise or a healthy lifestyle can be unique. Unique benefits can be a disproportionate draw and brand your company as cool.
About Your Company
Give your candidates the hard sell. Tell them why you're amazing, what sets you apart, or why you're poised for growth. You want candidates to picture themselves working for you and being excited at the prospect.
Diversity, Inclusion, and Equal Opportunity Employment
A statement on diversity, inclusion, and equal opportunity employment is standard practice. Review this section with your human resources (HR) and legal departments to ensure you are complying with company guidelines and applicable laws in your region.
Legal, COVID, Data Privacy
Legal, health, and privacy departments may recommend additional information to be included in your data scientist job posting. Some candidates will want to know safety protocols relating to COVID. Do your due diligence in complying with your regional standards.
Job Description A/B Testing
Woohoo! The job description is done and you're ready to accept applications. To receive as many applications as possible, you should consider A/B testing. This is an advanced approach that will be more valuable for larger organizations. Smaller organizations may not receive enough applicants to make A/B testing worthwhile.
A/B testing is a method of running experiments to optimize your product. In the context of a job description, it means when a prospective job applicant visits your site, some of the users will see "job description version A" and some of the users will see "job description user B". You then measure the number of applicants that apply to each version. You can then use data science to analyze the number of applicants to each version of the job posting to determine which one is more effective.
Over time, A/B testing allows you to test different titles, headers, and copy. Keep trying different variations of your job description to discover what works best!
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