In today's digital age, companies that fail to leverage data-driven decision-making to optimize their business outcomes will quickly fall behind the competition, making data scientists a necessity. Tasked with finding solutions to critical business processes by using their advanced computational and mathematical expertise, data scientists write algorithms, create databases, and create digital models of real-world scenarios to help companies find the best path forward in all their operations.
Such cutting-edge work demands a data scientist that not only has a highly technical toolkit but also has enough business acumen to navigate practical business applications and is able to articulate their findings to non-technical personnel. That can be a difficult combination to find, and it's why data science is consistently one of the most in-demand (and high-paying) jobs in the country. It's also why creating an attractive data scientist job description is so important for finding and hiring a data scientist that's right for you.
In this article, we'll provide a job description template that companies can use to outline the duties of a data scientist within their organization, some daily responsibilities, the required qualifications, hard and soft skills, and the average compensation they receive. We’ll also cover how Revelo can help you find the best fit for your organization.
Data Scientist Job Description Template
Until recently, the field of data science has been relatively undefined. Much work remains in forming a clear-cut definition of what data science does and doesn't entail, which has led to a widely varying job description for data scientists as a whole. This is why versatile candidates with fluency in mathematics, programming, AI, and even business processes, also known as "data science unicorns," have been so highly sought — companies need data scientists that do it all.
Despite the lack of clarity surrounding their scope of practice, some details should be part of a data scientist's job description. Follow this template to see what you should include.
Data scientists not only possess a deep technical skillset consisting of programming, statistics, data visualization, and more, but their skills are valuable in many applications. Some possible projects that data scientists are likely to find themselves working on include the following:
- Creating data pipelines and repositories to use for future analysis
- Writing advanced machine learning and artificial intelligence algorithms
- Building predictive models to anticipate future business opportunities
- Working with interdepartmental teams to identify and develop future products or areas for growth
- Displaying their findings to non-technical personnel using data visualization techniques
Increasingly, artificial intelligence (AI) have also led to a need for data scientists to contribute to conversations surrounding data ethics and the proper use and limitations of new technology. From algorithms and statistics to presentations or ethics, a data scientist's job may vary daily.
Data Scientist Responsibilities
Because data scientists' skills are so multifaceted, their responsibilities are equally diverse. While many of their duties vary from business to business, some of the most common data scientist responsibilities are:
- Identifying and collecting valuable data sources
- Wrangling and cleaning unstructured raw data
- Building data models to reflect real-world scenarios
- Applying knowledge of mathematics, including calculus, linear algebra, probability, and statistics, to analyze massive datasets
- Developing algorithms to power machine learning and artificial intelligence (ML/AI) technology
- Applying their business intelligence to minimize operational costs, anticipate market trends, and optimize business processes
- Creating charts, graphs, and presentations to articulate their ideas to stakeholders
Most of a data scientist's responsibilities involve using mathematics, programming, and BI to derive informed solutions from massive raw data, but they must also work with other departments for many applications. Other responsibilities could include contributing to product development, assisting sales and marketing teams with refining their operations and supervising a team of data analysts.
Data Scientist Qualifications
The average data scientist has significant training in some form of IT-related field. Their qualifications may vary by the company, years of experience, and several other factors, but the most common criteria include the following:
- Education level: A college degree isn't always required, but the typical entry-level data science position often requires at least a bachelor's degree. Computer science, mathematics, computer engineering, or IT are the most common degrees, but a data boot camp or certificate could count instead.
- Programming expertise: Data scientists must be fluent in multiple programming languages to perform their everyday tasks. Python and R are a must, and SQL, MATLAB, SAS, and Spark are helpful too.
- Business intelligence: Prior business experience isn't necessarily required, but a working knowledge of business concepts can help data scientists find solutions to problems more easily. It can help them communicate with stakeholders and executives too.
Experience level is another important qualification for many data scientist positions. While many employers expect data scientists to have worked as data analysts or in another IT-related field, some internships exist to give incoming data scientists the practical skillsets they need.
Data Scientist Skills
Their versatile skillset requires data scientists to have various soft, hard, and technical skills. Finding a candidate that possesses them all can be challenging, so vet your applicant for these skills both in your job description and again during the interview process.
- Leadership potential
- Written and verbal communication skills
- Ability to work in a team
- Fluency in mathematics, including calculus, linear algebra, statistics, and probability
- Knowledge of programming languages, such as Python, R, SAS, MATLAB, etc.
- Knowledge of business intelligence (BI) principles
- Data visualization with tools like Tableau, Excel, etc.
- Proficiency with machine learning and artificial intelligence (ML/AI) tools, including Hadoop, Spark, etc.
- Knowledge of cloud and edge computing
Visit our in-depth data scientist interview question guide to learn more about how to vet these skills during the interview process.
Compensation and Benefits
When hiring data scientists, it's important to list a salary range that matches the experience you are looking for. Other benefits you should mention may include health insurance, paid time off, and 401(k) matching, but the modern American employee would also find benefits that promote greater work-life flexibility to be highly attractive — one reason the tech sector has one of the highest rates of remote or hybrid work.
Data scientists are in high demand, and their work is notoriously rewarding. To attract the top talent, you'll need to explain your company's mission, vision, and values and why they should work for you.
Hire Data Scientists With Revelo
Hiring the right data scientist for your team is essential to maximize your data-driven decision-making in today's digital age, but finding the right one is no easy task.
Revelo's talent marketplace gives businesses access to the deeply skilled and professional data scientists that companies need to optimize their data infrastructure — and at a fraction of the cost of hiring American employees. Our data scientists are time zone-aligned and are rigorously vetted to ensure they possess the technical training and communication skills that would qualify them for a typical position in data science. Our team also handles payroll, benefits administration, taxes, and local government compliance, so you can focus on growing your business. Contact us today to scale your team and hire top notch data scientists.