Data scientists are responsible for transforming the world’s growing ocean of data into valuable and actionable information. With such a niche and in-demand skill set, connecting to individuals with data science expertise is easier said than done.

To effectively attract and hire data scientists, companies will need to craft job descriptions that sell candidates on the opportunity of the position. In this post, we break down how hiring managers and recruiters can write successful data scientist job descriptions.

What Does a Data Scientist Do?

Before writing a compelling job description, companies should have a clear understanding of data science and the core requirements of the role.

Companies of every size and industry need data to make informed business decisions. Doing so requires people with knowledge of statistics and data modeling to unlock the value in unprecedented amounts of raw data.

Data scientists use statistics, data analysis, and computer science to transmute unprocessed data into actionable insights.

On a more technical level, the core job responsibilities of data scientists include:

• Using database tools and programming languages to obtain, manipulate, and analyze data
• Building natural language processing applications
• Creating machine learning algorithms across traditional and deep learning
• Analyzing historical data to identify trends and support optimal decision-making
• Communicating with both technical and non-technical stakeholders 
• Keeping up-to-date with advancements in technology

What Should a Job Description Include?

Company Value Proposition

In 2020, there was a shortage of 250,000 data scientists across the world. With this level of demand for their skills, data scientists have an endless array of opportunities to choose from. So, what will make your open roles stand out? The first section of a job description needs to address this question.

Communicating the opportunity of the role at hand – along with the employer brand, value proposition, and company culture – is essential in crafting a successful job description.

Responsibilities

The responsibilities section lists the core activities of the role. There are a number of different ways to represent these responsibilities, including daily tasks, monthly responsibilities, objectives of the role, and key outcomes. 

Common data science responsibilities include:

• Analysis
• Coding
• Planning
• Communication
• Collaboration
• Storytelling

Basic Qualifications

The basic qualifications section has two main components.

First is the required degree level. A common qualification for data science roles is for the candidate to have a minimum of a bachelor’s degree, though it’s also common for the role to require a master’s or even a doctorate. One study found that 88% of data scientists have a master’s degree or higher — and 48% had a doctoral degree or higher.

Second is the required years of experience scaled to the seniority of the role. This requirement is communicated as either a range or a minimum requirement:

• Entry-level: 0-2 years
• Mid-level: 3-5 years
• Senior-level: 5+ years

Because of the field’s heavy emphasis on graduate degrees, data scientists often begin their careers at the senior level. Companies looking to hire data scientists with advanced degrees need to account for this fact while communicating the experience level of the position. For example, a company hiring a senior data scientist would consider the following experience levels equivalent:

• PhD with 2+ years of experience
• MS with 4+ years of experience
• BS with 6+ years of experience

Lastly, some companies use this section to list experience with a minimum number of programming languages as a basic qualification.

Required Qualifications

The required qualifications section is one of the core sections of a job description. While basic qualifications focus on education and experience, this section gives employers the space to list the must-have technical competencies and soft skills required by the role. 

Common technologies found in this section include:

• Python
• R
• SQL
• MySQL
• NoSQL
• Oracle
• C/C#
• Go
• MATLAB
• Java
• PHP
• Ruby
• Scala
• AWS
• Azure
• GCP

Common technical competencies found in this section include:

• Advanced knowledge of machine learning and artificial intelligence
• Experience with big data tools
• Ability to engage in data storytelling
• Extensive experience with relational databases
• Domain expertise in a particular industry or use case
• Knowledge of cloud computing and infrastructure
• Experience working in an agile environment
• Knowledge of industry-wide technology trends and best practices

Common mathematical skills found in this section include:

• Statistics
  • Probability theory
  • Hypothesis testing
  • Regression
  • Nonparametric statistics
• Linear Algebra
  • Vectors
  • Matrixes
  • Tensors
  • Linear systems
• Calculus
  • Differentials
  • Integrals
  • Partial Derivatives
  • Directional gradients

Common soft skills found in this section include:

• Communication
• Technical communication
• Project management
• Time management
• Problem solving

Other Desired Skills & Nice-to-Haves

While not present on every job description, some companies will choose to mention additional skills that the company desires applicants to have, but are not required to succeed in the role. Examples of nice-to-haves for a data science role would be experience with visualization tools such as Tableau, experience with Apache Hadoop, or an understanding of JavaScript.

Sample Job Description

Job descriptions for data science roles can vary widely, depending on the responsibilities, compensation, and seniority of the position. That said, there are commonalities between descriptions that companies can take advantage of. Here’s an example of a job description for a senior-level data scientist role:

Title: Senior Data Scientist

Full-time. Associate.

Responsibilities

Analysis – Leads and participates in the development, validation, and delivery of algorithms, statistical models, and reporting tools.

Delivery – Delivers agile solutions aligned to business needs while maintaining a high standard of quality.

Collaboration – Partners with product owners to understand business requirements to translate them into intelligence solutions. Spreads data best practices to the team.

Communication – Clearly communicates roadmap, backlog, and team updates across the organization. Communicates actionable insights to non-technical stakeholders.

Qualifications

Basic Qualifications

• 3-5 years of data science experience mining actionable insights from data sets.
• Programming experience with at least two languages.
• PhD with 2+ years of experience, MS with 4+ years of experience, or BS with 6+ years of experience in a quantitative discipline.

Qualifications

• 3+ years of hands-on experience with relational databases (SQL, MySQL)
• 3+ years of experience in machine learning (regression, classification, clustering)
• Experience using C, C++, R, Python, or Go
• Knowledge of industry-wide technology trends and best practices.
• Strong mathematical skills (statistics, calculus)

Other Desired Skills

• Experience with Apache Spark, Apache Hadoop, and TensorFlow
• Understanding of visualization tools and front-end programming languages