{"id":2371,"date":"2022-06-17T10:00:47","date_gmt":"2022-06-17T17:00:47","guid":{"rendered":"https:\/\/www.springboard.com\/?p=2371"},"modified":"2025-01-27T04:39:50","modified_gmt":"2025-01-27T12:39:50","slug":"data-scientist-job-description","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/data-scientist-job-description\/","title":{"rendered":"Data Scientist Job Description: What to Expect in 2025"},"content":{"rendered":"\n

It\u2019s been said that you can\u2019t improve something that you can\u2019t measure. And so, in today\u2019s digital landscape, where every interaction becomes a measurable data point, data scientists are increasingly in high demand. <\/p>\n\n\n\n

The job of a data scientist now ranks sixth on U.S. News\u2019 \u201c100 Best Jobs\u201d list. And it\u2019s easy to see why. Data scientists solve real-world problems, which is why many data scientists (even entry-level ones) make more than a hundred thousand dollars a year. From healthcare to tourism, almost every industry has data that needs to be analyzed. <\/p>\n\n\n\n

Want to study trends in patient data to improve success rates for cancer treatments? You\u2019ll need a data scientist for that. <\/p>\n\n\n\n

Want to track and predict the likelihood of certain mood disorders in a specific community? You\u2019ll need a data scientist for that too. <\/p>\n\n\n\n

Do you love traveling and want to help tourism companies offer better travel experiences by using customer sentiment analysis? Better hire a data scientist. <\/p>\n\n\n\n

These are just a few examples of niches that need data scientists, and the list goes on and on. <\/p>\n\n\n\n

Want to learn more about what a data scientist does, what skills are required, and what data scientists can expect to make? Then keep reading.<\/p>\n\n\n\n

What Is a Data Scientist?<\/h2>\n\n\n\n
\n
\"YouTube
<\/path><\/path><\/svg><\/div>

A data scientist<\/a> is someone who can find meaning in data. Everyone solves problems with intuition, imagination, logic, or knowledge. Data scientists solve problems through data, and they provide solutions to business challenges and help with data-driven business strategies. <\/p>\n\n\n\n

\"Data
Source: Data Camp<\/a><\/figcaption><\/figure>\n\n\n\n

What Does a Data Scientist Do? <\/h2>\n\n\n\n

Let\u2019s answer this question with a hypothetical. Say that Cory is a data scientist working for McDonald\u2019s.<\/p>\n\n\n\n

To help improve customer experience and sales, Cory builds models using data. McDonald\u2019s is looking to market a new, more premium, healthy choice meal. And so Cory is trying to answer the following question:  \u201cWhat\u2019s the most enticing meal combination that still meets health guidelines?\u201d <\/p>\n\n\n\n

Here are the steps that he\u2019ll take to answer this question:<\/p>\n\n\n\n

Discovery <\/h3>\n\n\n\n

In the discovery phase, Cory will try to understand the nuances of the question he\u2019s trying to answer. So he\u2019ll ask questions about their target customers\u2014including demographic information, customer segment information, their pain points, and why they like to eat at McDonald\u2019s.<\/p>\n\n\n\n

Acquire Data <\/h3>\n\n\n\n

Then, Cory will look for data, which can include combing through McDonald\u2019s past sales records to pinpoint patterns related to \u201chealthier\u201d items. Cory will also look for guidelines that qualify what constitutes a \u201chealthy meal.\u201d Finally, he\u2019ll go through McDonald\u2019s menus at different locations, collecting nutritional information about their offerings.<\/p>\n\n\n\n

Data Processing<\/h3>\n\n\n\n

All this data will be in its raw form, meaning it is unstructured and in silos. So before he can start to analyze this data, Cory needs to process it, which requires scrubbing and cleaning the data to make it consistent. Missing variables, wrongly recorded quantities, and incorrect values are just some of the gaps that Cory will fill.<\/p>\n\n\n\n

Data Integration<\/h3>\n\n\n\n
\"data
Source: Tech Target<\/a><\/figcaption><\/figure>\n\n\n\n

Now that Cory has processed all of this data, he can integrate it into a unified hub of data points that can be analyzed. <\/p>\n\n\n\n

Data Investigation<\/h3>\n\n\n\n

Now, Cory will trim, supplement and refine the data. This will help him determine whether he needs to revisit previous steps. For example, has he checked for calorific values across different branches in the area? Does he need data on sales from ten years ago, when the definition of a healthy meal was different?<\/p>\n\n\n

Get To Know Other\tData Science Students<\/h4>
\"Samuel<\/a>

Samuel Okoye<\/p>

IT Consultant at Kforce<\/p><\/div>

<\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Sam<\/a>

Sam Fisher<\/p>

Data Science Engineer at Stratyfy<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div>

\"Melanie<\/a>

Melanie Hanna<\/p>

Data Scientist at Farmer's Fridge<\/p><\/div>

Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n

Exploratory Data Analysis (EDA)<\/h3>\n\n\n\n

Cory will then analyze the vast amounts of data sets to cull common characteristics and identify any outliers. He can do this using Python<\/a> or R.<\/a><\/p>\n\n\n\n

Cory will also screen the data to ensure that certain assumptions (i.e., that cheese is an unhealthy component) are valid. Finally, Cory will label his data across various classifications\u2014such as continuous, discrete, and categorical\u2014which will dictate the techniques he uses to analyze this data.<\/p>\n\n\n\n

Implement Data Science Techniques <\/h3>\n\n\n\n

Cory will then use tools to cull insights from the data. These tools could include:<\/p>\n\n\n\n

Machine Learning<\/strong><\/h4>\n\n\n\n

ML tools can automate parts of the project’s life cycle, including collecting and cleaning data.<\/p>\n\n\n\n

Statistical Modeling<\/strong><\/h4>\n\n\n\n

When there is a vast expanse of data, Cory might prefer visualization for the intuitive recognition of patterns. Instead of scanning pages of historical sales data numbers, Cory can produce graphs to identify relationships.<\/p>\n\n\n\n

Artificial Intelligence<\/strong><\/h4>\n\n\n\n

AI is an umbrella term for machine learning, deep learning, etc. Cory can use simple or advanced AI tools to analyze large datasets of the menu items, ingredients, and nutritional values.<\/p>\n\n\n\n

With all these tools, Cory might create predictive modeling<\/a> tools to cover various food combinations.<\/p>\n\n\n\n

Measure, Analyze, and Improve Results<\/h3>\n\n\n\n

Cory will now measure the results that he\u2019s generated and analyze these results to produce insights. <\/p>\n\n\n\n

He might find that the ways of communicating nutritional value need to be changed to encourage healthier choices. For example, younger folks may prefer an info-packet that includes dietary values to make their decisions.<\/p>\n\n\n\n

Once he\u2019s measured and analyzed his results, Cory might also find ways to improve his original techniques. For example, he might consider a longer time range, different variables, or a \u201cmicro versus macro\u201d approach.<\/p>\n\n\n\n

Data Scientist Job Description<\/h2>\n\n\n\n

No single data scientist job description can be considered \u201cthe\u201d job description. Each organization requires different things from their data scientist teams, so the qualifications and requirements may differ depending on who you work for. However, there are some commonalities across most data science job descriptions. Here are two examples from Lego and Twitter, two completely different companies that both need data scientists:<\/p>\n\n\n\n

\"Data
Lego Data Scientist Job Description – Background<\/figcaption><\/figure>\n\n\n\n

Data Scientist Qualifications<\/h3>\n\n\n\n

As you can see in the two previous job descriptions from Lego and Twitter, the following degree requirements are preferred:<\/p>\n\n\n\n

    \n
  1. Bachelor\u2019s degree in computer science, mathematics, or an adjacent field like economics, information management, statistics, or business information systems.<\/li>\n\n\n\n
  2. Post-graduate degree in business analytics, data science, big data, etc.<\/li>\n\n\n\n
  3. Advanced degree such as a Master\u2019s or Ph.D. in operations research, data mining, machine learning, electrical engineering, etc.<\/li>\n<\/ol>\n\n\n\n

    Note that both job descriptions include the phrases \u201cor other quantitative description\u201d or \u201cin relation to\u2026\u201d  This is likely because data science<\/a>, as a field, is rapidly expanding, and many companies see core degrees as a preference, not a prerequisite, if you can demonstrate your proficiency in other ways.<\/p>\n\n\n\n

    Data Scientist Technical Skills<\/h3>\n\n\n\n

    The job descriptions from Twitter and Lego both require certain technical skills. Robert Chang, a data scientist at Airbnb, advises aspiring data scientists to not worry about learning everything. Instead, he advises that you focus on just learning the skills you\u2019ll need for the job that you want. However, all data scientists should learn R or Python and some SQL. <\/p>\n\n\n\n

    Let\u2019s break down some of the technical skills that both of these jobs require: <\/p>\n\n\n\n

    Statistical Analysis<\/strong><\/h4>\n\n\n\n

    This means collecting, organizing, analyzing, and interpreting data to present key findings. Statistical techniques include hypothesis testing, standard deviation, regression, etc.<\/p>\n\n\n\n

    Machine Learning<\/strong><\/h4>\n\n\n\n

    This will help you at every stage of your statistical analysis and during other funnel phases. It can help automate data collection and scrub data with minimal manual work. You can also use machine learning techniques to analyze data and train models that will make predictions based on the patterns and networks in your data set.<\/p>\n\n\n\n

    Programming<\/strong><\/h4>\n\n\n\n

    A data scientist job description will include one or more of these programming languages\u2014R, Java, Perl, SQL, Python, C\/C++, etc. These will add nuanced analysis to your data sets. You can simplify your data sets and get to your solution faster if you can write programs in these languages. Consider enrolling in free online programming language bootcamps and certified courses offered by universities.<\/p>\n\n\n\n

    Computer Science<\/strong><\/h4>\n\n\n\n

    Most data scientist job descriptions require some familiarity with Hadoop, Apache Spark, and NoSQL platforms. All of these allow data scientists to process data more efficiently.<\/p>\n\n\n\n

    Data Visualization<\/strong><\/h4>\n\n\n\n

    Mastering Tableau, or a few of these free data visualization tools<\/a>, will help you share your data insights through graphs, charts, and other visual aids. <\/p>\n\n\n\n

    Data Storytelling<\/strong><\/h4>\n\n\n\n

    Spiffy data visualizations aren\u2019t enough to communicate your findings. You\u2019ll also need to tell the story of your data science process<\/a> to give your team actionable insights. <\/p>\n\n\n\n

    Data Scientist Soft Skills<\/h3>\n\n\n\n

    Here are some soft skills that are essential to any data science role:<\/p>\n\n\n\n

    Analytical Thinking<\/strong><\/h4>\n\n\n\n

    Data scientists with strong analytical skills help companies save time and money. Analytical thinking will help you understand the question at hand, identify what you need to solve it and recommend a course of action. <\/p>\n\n\n\n

    Business Intelligence<\/strong><\/h4>\n\n\n\n

    When working for a corporation, having a larger perspective on the goals of the business is crucial for any data scientist. This means having some business acumen. If you don\u2019t know the context of the problem you\u2019re trying to solve, then you won\u2019t be able to generate valuable insights.<\/p>\n\n\n\n

    Critical Thinking<\/strong><\/h4>\n\n\n\n

    Data science is all about using data to find a solution to a problem. Therefore, you need to have the necessary thinking skills and natural curiosity to get to the root of the question and apply multidisciplinary (statistical, business, analytical) approaches to the issue. If you cannot identify and analyze the question, no amount of programming skills or data sets will help.<\/p>\n\n\n\n

    Data Scientist Salary<\/h3>\n\n\n\n

    What you make as a data scientist will depend on a range of factors, including your qualifications, your job title, company size, industry, and region. <\/p>\n\n\n\n

    \"data
    Source: The Burtch Works<\/a><\/figcaption><\/figure>\n\n\n\n

    But the biggest factor in determining your salary will usually be your level of experience. According to the 2021 Burtch Works Study on DS jobs, the median salary ranges that most data scientist job descriptions will reflect are:<\/p>\n\n\n\n

    \"data
    Source: The Burtch Works<\/a><\/figcaption><\/figure>\n\n\n\n