Hire the Top 3% of Freelance Data Scientists

The Demand for Data Science Tops the Charts Across Many Sectors

With a projected employment growth rate of 36% over the next decade (one of the highest compared to an average growth rate of 4%), data science has a long life ahead of it—and 90% of leading companies have recognized this fact by increasing their investments in generative AI as of 2024.

Yet, data science is not a simple field to master—or hire for—due to its many required proficiencies. As sustained demand in the job market increases, there is a race to find vetted data scientists who can analyze data carefully, build unbiased algorithms, and present compelling insights to stakeholders.

At a minimum, data scientists need an extensive background in statistics and programming, and strong experience with production data sets and models. This guide specifies the job description tips, interview questions, and project-specific skill requirements that inform how to hire data scientists and maximize your company’s data analytics capabilities.

What Attributes Distinguish Quality Data Scientists From Others?

Top-notch data scientists should have a blend of statistical, programming, and business skill sets with corresponding experience. At a minimum, an experienced data scientist will be proficient in four key competency areas:

• A pragmatic, statistical, and data-driven mentality – Handling data, especially big data, requires a foundation in statistics and an understanding of potential pitfalls and biases. Data scientists must comprehend potential technical risks, such as selection bias, survivorship bias, or Simpson’s paradox.
• Good communication and business understanding – Data science is highly interdisciplinary. Data scientists should be able to translate business needs into practical solutions, present the insights gained, and explain answers in layperson’s terms. Soft skills are essential for communicating complex solutions and insights to both technical teams and stakeholders.
• Experience with programming languages and databases – To handle, analyze, and present data, data scientists must be proficient with a programming language (typically Python) and possess experience in querying databases (typically SQL databases, though NoSQL database skills may be required depending on your project).
• Experience with production data sets and models – High-quality candidates will have real-world experience with production data sets and models instead of having only used test data sets such as those found on Kaggle (i.e., data competition experience). Data competitions don’t teach all the skills needed to work with real-world data.

But what does a data scientist do, exactly? There is no simple answer. Data scientists are versatile, creative thinkers who can generate value from raw data in many ways—and they must have mastered many different concepts.

With a high-level overview of data science proficiencies and results, let’s further break down the tangible data science skills required for success:

• Python – The ubiquitous language among data scientists and machine learning developers.
• SQL – The language typically used by data scientists to communicate with databases; most candidates should at least have rudimentary SQL experience.
• Statistics – The core mathematical foundation of data science that is crucial for data scientists to reduce biases, verify conclusions, and decide which model to use.
• Data wrangling – The ability to transform raw data into a usable form; data scientists use this skill to clean and organize data during the extract, transform, and load (ETL) process.
• Data visualization – The visual presentation of data insights used to communicate key findings and verify results; data scientists should understand how to visualize and interpret data specific to your problem to ensure relevancy and avoid harm.
• Machine learning – The ability to train models on past data to perform on unseen data; at a minimum, data scientists should know simple machine learning models.
• Cloud computing – A key component of modern data-driven businesses; data scientists should be prepared to use cloud tools alongside models in cases requiring training, heavy computing power, or production deployment.

Finally, general developer skills like debugging and using version control tools (e.g., Git is most commonly used for version control) are also mandatory for data scientists working with code.

How Can You Identify the Ideal Data Scientist For You?

There are multiple considerations when finding a data scientist who matches your project requirements. When working with complex data or on more technical efforts, including research and automation, you should focus on specialized candidates.

For all types of projects, to ensure you have a good fit, explain your problems, your business goals, and the data available, and then ask the candidate to describe their relevant experience.

Complex data—text, images, audio, video, and time-dependent data—should be treated carefully, as it is handled very differently from tabular data and requires special training and methods. In this case, a candidate should provide a detailed synopsis of similar projects they have worked on previously and how they will apply their skills to your project.

If you are working with simpler data (e.g., structured, clean data), you may be able to meet your needs with a less technical data analyst. When should you hire for data science versus data analyst skills? This is a standing debate in the community, and there is no universal answer. However, some differences are generally agreed upon:

If your project includes advanced technical goals—performing task automation, solving open research problems, or implementing global business improvements (e.g., researching how AI models improve business needs)—then your needs extend beyond simple data analysis, and you should focus on hiring data scientists.

When proceeding with a data scientist, you will benefit from identifying the precise specialization under the umbrella of data science that your project requires:

• Data mining specialists extract information from large data sets.
• Data engineering specialists format and structure data for analysis.
• Database management specialists organize data on a company-wide scale.
• Data visualization specialists prepare interactive visual representations of data.
• Machine learning specialists create advanced models to solve complex problems.

Commonly, multiple data science experts across varying specializations will work together to achieve a team’s goals.

How Do You Write a Data Science Job Description?

When you have identified the skills necessary for a quality data scientist and your project-specific requirements, writing your job description is the next step. Your job description should include:

• The data at hand, problem statement, and project goals (e.g., analysis, visualization, prediction model creation, data cleaning, etc.).
• The technology stack and available resources, including the project’s software languages and frameworks, cloud providers required, and database type.
• The flexibility data scientists will have in how they can approach the problem, which models they can use, and what the data processing pipeline might look like; good candidates will be able to suggest different approaches tailored to your problem.

You may reference a data scientist job description template as a starting point and adjust it depending on your needs to pinpoint the best data scientist for the job.

Data science is a highly technical role, and it is important to verify a candidate’s background with multiple assessment rounds once you have identified suitable applicants from your job posting. It may be helpful to prepare a screening test with standard programming and theoretical questions before interviewing. Also, you may want to vet senior data scientists with a take-home project with deliverables relevant to your company’s goals.

What Are The Most Important Data Science Interview Questions?

Your selected data science interview questions will be informed primarily by your business requirements. However, there are some standard questions all data scientists should answer correctly before moving on to your project-tailored questions.

You may start with basic data science concepts as a warm up. A candidate who cannot answer these questions may not have an adequate data science background to move forward:

What is a graph, and why is it useful?

A graph (or network) is a data structure generally used to make data analysis and visualization easier. It represents information using nodes connected by edges:

• Nodes represent entities such as a person, an address, or a movie listing.
• Edges connect nodes; they represent relationships between nodes.

Let’s consider a simple example: A graph might have a user node connected to other nodes representing related user information (e.g., the user’s residence country or several of the user’s topics of interest). Businesses can use this graph and all of its information for applications such as producing recommendations tailored to each user.

How is SQL used in data science?

SQL is the standard language used to make queries when working with relational databases. It can make simple queries (e.g., fetching all users older than 21) and complex queries that aggregate or calculate statistical values and other counts. For example, a more complex query might identify all users older than 16, group them by their jobs, and return their sorted count, average credit score, and average salary.

After verifying a candidate’s knowledge of data science basics, you should assess their understanding of skills related to working with large amounts of data—these are modern data science necessities:

What can you do with data wrangling?

Data wrangling makes data sets easier to analyze and interpret. It is a necessary step when the starting data is not well organized or lacks a standard structure. It typically formats values in a standard way, such as putting all dates and times in ISO 8601 format or organizing all phone numbers with prefixes. Data wrangling can also assist with data validation: For example, it could handle a case where a person’s age is 734 years or has a negative value.

What are the benefits of cloud computing in data science?

In short, cloud computing reduces machine learning costs. Machine learning models are typically resource intensive in the training phase. Though they can use any machine (e.g., a laptop) for testing, once models are validated and ready for real training, they require much more computation time and power—and, in many cases, specific hardware, which is extremely expensive to buy. Cloud computing allows data scientists to rent the hardware (and execute computation from the cloud), which makes training a model much more affordable.

We have covered basic data science questions applicable to many projects that act as a starting point and demonstrate the level of detail to expect in a candidate’s answers. However, every data scientist should be skilled in various programming languages and statistical concepts. You should cherry-pick additional questions from the following guides based on your requirements:

• 11 Essential Python Interview Questions and Answers
• 41 Essential SQL Interview Questions and Answers
• 10 Essential Data Analysis Interview Questions and Answers
• 10 Essential Machine Learning Interview Questions and Answers

Data scientists serve many different roles depending on a company’s needs; for such a broad role, there is no one-size-fits-all list of interview questions applicable to every project.

Why Do Companies Hire Data Scientists?

Modern companies collect and process large amounts of data daily, whether from their internal processes, their customers, or other external sources. After being treated, the data is stored and often left unused. If you sell any product, you likely have years’ worth of order history records lying around. Past data yields future value—with the right data scientist.

The short answer to the question “When should I hire a data scientist?” is “Almost always,” especially when you are working with large or complex data sets and want to make data-driven business decisions. In smaller businesses, a data scientist can set up a data pipeline and provide guidelines on collecting data based on the company’s future endeavors. For companies collecting larger amounts of data, a data scientist can provide insights, suggest data-driven decisions, and train prediction models.

Since data is highly company-specific and business concerns can vary widely, it’s difficult to make generalizations about a data scientist’s work. However, we can examine a few example scenarios:

• A data scientist can create a system capable of suggesting tailored recommendations for past and future clients.
• A data scientist can predict required maintenance, reducing unexpected repair costs.
• A data scientist can automate tasks currently done manually, saving countless hours of work per year.

Data science is increasingly becoming an essential aspect of business decision-making, automation, and analysis. It is wise to include data scientists in your company to provide better customer experiences, increase sales, and drive innovation. Businesses that don’t maximize the potential of data will be left behind, and hiring the best data scientists will allow your products to yield more value than those of competitors.

The technical content presented in this article was reviewed by Amanbir Singh.