Data science uses scientific methods, processes, algorithms, and systems to identify and extract data to generate useful information and business insights from large volumes of structured and unstructured data. Data scientists apply knowledge and expertise from across a broad range of fields and technical domains.
The profession of data science is continuing to grow and is a very in-demand role. A good data professional knows they must advance past the traditional data analysis skills, such as analyzing large amounts of data, data mining, and programming to find the most crucial intelligence for their organizations. To most efficiently process and achieve maximum benefits at each step of the data science lifecycle, they must have top-notch expertise across the spectrum of data gathering and analysis.
Data scientists typically work for corporations across all industries to provide them with meaningful insights about how to improve their business operations. They use their knowledge of coding and other computer programming tactics to automate data collection and storage tasks. Their job is to work closely with company departments to pull data about business decisions or initiatives to objectively measure their success. They are also responsible for creating systems for collecting and storing data within company databases.
Attracting an exceptional data scientist for your organization can be a difficult task, but we’ll give you some great examples and guidance to create the best data scientist job description that will bring the right people to you.
Download and edit in Microsoft Word.
Edit in Google Docs (choose "File" and "Make a copy").
Download and edit in Microsoft Word.
Edit in Google Docs (choose "File" and "Make a copy").
Download and edit in Microsoft Word.
Edit in Google Docs (choose "File" and "Make a copy").
Crafting an effective and well-written job description is not always easy. A data scientist role is especially difficult because it is a highly technical role that encompasses a broad range of skills and varies greatly from company to company. The biggest challenge you will have is keeping it from being too lengthy and staying focused on the most important things your data scientist needs to know and what they will need to do.
A data scientist must perform many complex tasks simultaneously. They are required to assess large amounts of data, quickly and efficiently identify essential data, and distill it down to the most valuable information. Likewise, your job description must be distilled to the essentials—nothing more.
Ensuring your potential candidates will read the entire job description is a major objective you must keep in mind as you write. The most essential thing you need to do is keep your job description brief and concise. Listing all the pertinent job requirements for a data scientist role without creating a lengthy document can be difficult, but make a concerted effort to include the key info and “kill your darlings” by eliminating the unnecessary.
Another major objective to remember is that you’re selling the job and your organization. You are trying to attract as many qualified applicants as possible. The best way to demonstrate what a job entails is by giving examples of issues a data scientist in your company will face. For instance,
Returning full circle, George Mason University’s writing center stresses the importance of condensing your writing. According to the experts, writing concisely means that you “write what you mean—nothing more and nothing less." When you create a data scientist job description, consider each word choice and look for extra words you can remove without changing the meaning. Remove filler words and extraneous language.
Focus your requirements on the critical and unique needs of your company, eliminating common data science skills or traits, such as “strong data analysis skills.” This fluff clogs up your job description, not to mention that listing too many requirements tends to discourage diverse candidates from applying, especially in STEM.
Writing a completely unbiased data scientist job description isn’t always easy despite your best efforts. It takes a conscious effort to use words and phrases that will encourage diverse populations to apply. MIT’s “Ensuring Bias-Free Job Postings” gives a high-level overview of the issue and has some pointers to help you avoid pitfalls that incidentally stereotype or alienate job seekers.
The final and most important step to creating a great job description is editing and revising. Invite people in the data science function to review your work as any additional review is beneficial. Seek out constructive criticism, and be willing to make changes if appropriate.
The worst thing you can do is submit a job description that has poor word choice, grammatical errors, or even worse, an inaccurate salary range with one too many zeros. Take your time, and when you’re done, you can be confident that you’ve published a high-quality listing that will attract the best talent for your data scientist role.
We’ve shown you the examples and stressed the importance of writing your job description well, but we get that those things can seem a bit arbitrary when you’re staring at a blinking cursor on a blank screen.
Check out the following outline that covers all the sections for any data scientist job description; then, all you need to do is fill it in, drawing upon inspiration from the examples and how-to guide above.
Job details: This section is where you briefly introduce the company and the open position. You should leverage this section to immediately grab the reader’s attention. Offer candidates a taste of why your company is a great place to work. Then, in a couple of sentences, give them a quick blurb on the what and why of your data scientist role to encourage job seekers to read the rest of your job description.
What you’ll be doing: You may also call this section the “Roles” or “Responsibilities” or “Requirements.” This section should be a bulleted list of the key tasks performed in the job role. Don’t make it too long. Ensure you include anything unique to or especially cool about the data scientist role in your company. Emphasize anything critical to the success of the position. As with any business writing, always use active verbs, avoid jargon and filler words, and be as specific as possible.
Qualifications: This section is the make-or-break section. It quickly tells candidates whether they are a good fit for the job. Craft this section carefully. Always include anything critical and must-haves for the position. A good job description will list education, experience level, certifications, and any special requirements.
Technical qualifications should always be described as specifically and objectively as possible. Consider whether you need to include soft skills at all. Perhaps you can discern that in an applicant’s data scientist resume or data scientist cover letter, saving room to list job-specific qualifications, such as text classification topic mining or speech enhancement.
As you know, data science can include a lot of very technical requirements. Focus on the job's critical aspects, but avoid having too long a list. If your company has a lot of unique or extraordinary requirements, then use most of this section to cover that. As with every part of your job description, be brief, specific, and clear.
Nice-to-have: If you have a lot of other skills that you are looking for, but they are not deal-breakers, then you may include these as a subsection to your qualifications. List all the things you consider to be key differentiators for candidates. Again, try not to list too many because this will discourage some candidates.
Benefits:
The benefits section is a brief but standard item to include in your job description. Where you place it in the overall format is not so important, though it shouldn’t be the first item. If your company has outstanding benefits and/or something extraordinary to offer, such as an onsite gym or an unlimited vacation policy, then you may want to place this section earlier to snag the reader’s attention.
About the company: The last section of your job description should contain additional information about your company. A data scientist will be interested in knowing a company’s strategy regarding data analysis, the usage of big data, and how the company leverages information technology. Cover these items, or anything directly related, in a sentence or two, noting the role of the data scientist.
Data scientists are researchers and masters of quantitative analysis. They investigate extremely large volumes of data and solve business challenges using data. Advanced data science involves identifying relevant data, detecting patterns and trends, and translating them into viable business plans and strategies. A data scientist must rapidly process and transform large data sets into understandable and usable business information.
Below are some sample roles a data science professional might fulfill. A single scientist may not fulfill all these roles, but this will give you are good list to choose from and add to based on your company’s specific needs.
Data Acquisition
Problem-solving
Communication and Reporting
Leadership
Training