Illustration showing a thumbs up symbol over a job description document and several data science related symbols, such as charts and math equations

With the abundance of information flowing in the digital age, companies turn to data scientists to find a deeper understanding of their data and how it can provide competitive advantages. Data science professionals use computer science, applied mathematics, visualization, and statistics to extract large amounts of information and find deeper insights and discoveries within the data.

What Does a Data Scientist Do?

Since data science is a relatively new field, the term “data scientist” can be applied to many different types of skill sets. Most candidates will have experience working with a statistical programming language like R, Python, Julia, or Scala, plus a background in SQL databases. Data scientists will also have some degree of proficiency in the fields of statistics, machine learning, linear algebra, data wrangling, data visualization, and software engineering.

Here are just a few of the areas that data scientists can excel in:

  • Organizing raw data in spreadsheets
  • Analyzing data and deciphering key takeaways
  • Creating charts and graphs to display results
  • Running split tests for marketing campaigns
  • Using machine learning to create new products
  • Creating data infrastructures for new businesse
  • Collaborating with other data scientists

Key Traits of a Data Scientist

Data scientists follow many different education and career and business paths to gain experience within the field. Some may have advanced degrees in mathematics, physics, or computer science, while others transition from statistical and analytical roles. The one trait that they all share in common, however, is the ability to use scientific method in order to analyze and verify results.

Here are a few more skills to look for:

  • Good at making statistically informed decisions
  • A quantitative approach to analyzing data
  • Solid overall technical abilities
  • Excellent verbal and written communication
  • Creativity to solve complex problems
  • Confidence in delivering predictions

Many data science professionals may also have prior experience in various business fields that would make them stand out. While specific industry knowledge is not always necessary, it can help some professionals better understand the client’s specific goals and requirements.

Finding and Attracting Great Data Scientists

It’s important to note that not all data scientists will be ideal fits for every project. For example, those with highly analytical backgrounds in software engineering would be ideal for developing algorithms but may not be the right fit for a data visualization project. That’s why it’s so important to understand what type of data scientist will bring the most benefit to your company and business goals.

Here are some questions to consider:

  1. What is the overall learning you hope to find? By including your goal in the project description, it allows professionals to better understand what type of work is required.
  2. What core skills will data scientists need to complete the project? The answer will revolve around your current data infrastructure and the processes used to extract information.
  3. Would you benefit from someone with highly specialized skills in a few areas of data science, or would a well-rounded expert serve you better?
  4. Are there any time constraints to consider with this project? Let professionals know the amount of hours of work that might be involved.
  5. What kind of budget will this project have? The more experience and expertise a data scientist has, the higher they expect to be compensated. Higher budgets will more likely give top-tier experts a reason to submit a proposal.

Sample Project Description

Below is a sample of how a project description may look. Keep in mind that many people use the term “job description,” but a full job description is only needed for employees. When engaging a freelancer as an independent contractor, you typically just need a statement of work, job post, or any other document that describes the work to be done.

XYZ Company is looking for an expert data scientist to help us study our website traffic patterns and find areas of improvement. This project is estimated to require approximately 20-25 hours per week for the next few months to achieve the following goals

· Organizing site data into spreadsheets
· Discovering which pages currently perform best
· Split testing underperforming pages and recording results
· Reporting findings in a weekly summary

The following skills are required:

· Expertise or extensive experience with Python
· A thorough understanding of SQL databases
· Experience with WordPress and Google Analytics
· Knowledge of quantitative split testing
· Excellent technical abilities

The ideal freelancer will be a creative problem solver with an excellent work history on Upwork. To submit a proposal, please send a short summary of similar projects you’ve completed and why we should consider you for this project.

Read this article for more tips on how to write a great project description.

Selecting the Right Data Scientist

Remember that technical ability is only a small portion of what makes an excellent data scientist. Great data scientists are inquisitive—they want to ensure that they’re seeking the right types of answers, plus they’ll take an interest in your business to better understand it. The ideal professional will also be able to advise you on additional metrics to analyze and compare in order to help you meet your goals.

Also, keep in mind that communication is always a key consideration in the data science field. A brief interview can allow you to gauge how strong each professional is in expressing ideas and explaining their process. The more you speak to each professional by phone, email, or chat, the better you’ll be able to gauge their professionalism and communication skills and determine whether they’re right for your project.

Browse freelance data science professionals on Upwork and start gleaning insights from your data today.