What Does a Data Scientist Do?

Data scientists leverage big data to generate insights for companies and organizations, helping them discover inefficiencies, customer pain points, and opportunities.

To become a data scientist, you’ll need a solid foundation of technical skills in math, statistics, programming languages like R and Python, as well as an aptitude for visualizing data using statistical and graphical techniques.

What Is a Data Scientist and What Does a Data Scientist Do?

Data science is an extremely broad field, entailing everything from cleaning data to deploying predictive models. It’s rare for a data scientist to do it all. Most data scientists specialize in a specific function within the data processing cycle.

Data scientists work in tandem with data analysts, data engineers, business intelligence specialists, and data architects to create and maintain databases, analyze data, and communicate business insights. Their job is to identify the data analytics problems that offer the greatest opportunities to the organization.

What Is the Average Salary of a Data Scientist?

Data scientists earn an average salary of $123,390/year, according to Indeed. Salaries vary widely by city, commensurate with cost of living.

The highest-paying cities include San Francisco ($159,217); New York City ($139,037); and San Diego ($136,767).

As for entry-level data scientists, they still earn a decent living at $104,995, per Glassdoor.

Learn more about data scientist salaries here.

data scientist salary

Data Scientist Job Description Template

A typical data scientist job description might look something like this:

  • Job overview. This section will define the scope of the role. It will summarize the position, the key skills required, and the day-to-day responsibilities of the data scientist. It will also include language about technical and interpersonal skills, and highlight which stakeholders the data scientist will need to work with.
  • Key responsibilities. This section will go into further detail about the day-to-day responsibilities and can include things like mining and analyzing data; working with key stakeholders within a company and outside of it; using predictive modeling; developing A/B testing frameworks; and developing custom data models and techniques.
  • Key requirements/qualifications. This section will outline the key qualifications and certifications needed for the role, including using statistical computer languages (R, Python, SLQ, etc.) to draw insights from data sets; any educational qualifications and coding experience; any experience analyzing data from third party providers such as Google Analytics, Site Catalyst, Coremetrics, Adwords, and Facebook Insights; and experience with machine learning algorithms and statistics including regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, and so on.

Learn more about a typical data scientist job description in this video.

2 Essential Skills a Data Scientist Needs

Data scientists need a wide range of skills, from technical proficiency in programming languages and statistical software to communication and storytelling. Companies that are looking to hire data scientists will expect potential candidates to perform statistical analysis and help them uncover vast amounts of information to drive smarter decisions and deliver better products.

Data scientists need two main types of skill sets: technical and interpersonal.

  • Technical skills. Data scientists need a solid understanding of mathematics and statistics, as analyzing data requires knowledge of statistical models. In addition to knowing how to manipulate data, data scientists need to be literate in sophisticated statistical modeling software, such as SQL databases and the Hadoop platform. Finally, it helps to have some knowledge of programming, since it vastly expands the types of modeling functions you can perform on any software application. What’s more, Python and R are considered the de facto programming language for data science, so proficiency in both is crucial.
  • Interpersonal skills. Effective communication is the most crucial non-technical skill data scientists have. Data scientists need to be effective storytellers in terms of how they visualize data and communicate their findings to key stakeholders who might be non-technical.

Learn more about the types of essential skills a data scientist requires here.

What Qualifications Do Entry-Level Data Scientists Need?

To qualify for an entry-level data science role, you’ll need high proficiency in SQL, intermediate to good statistical/mathematical computing in R/Python, data visualization skills (Excel or Tableau is fine, but experience with more advanced statistical tools could help you stand out).

You’ll also need to demonstrate a knowledge of machine learning algorithms and when to apply them, as well as an understanding of traditional and Bayesian statistics.

In terms of soft skills, recruiters will want to see that you’re able to communicate complex data science concepts to non-technical stakeholders.

Recruiters also want to see examples of projects that demonstrate these different skill sets. For instance, an active Github profile could show off your coding skills, while reports, graphs, and other data visualizations in your portfolio indicate that you’re familiar with statistical modeling software. A website with an interactive table or chart shows you have both.

Examples of Data Science Projects

Below are a number of examples of data science projects by former Springboard students.

  • Machine-powered investing. For his capstone project, Springboard data science alum Paul Brume (2018) decided to investigate peer-to-peer lending services, which allow users to lend and borrow money outside of the traditional banking system. Specifically, he was interested in how these companies evaluate credit risk differently from traditional banks. He investigated several factors relating to the likelihood of loan default, such as:

    1. Home mortgage status
    2. Annual income
    3. Employment history
    4. Requested loan amount
    5. Credit age

    For each factor, Paul made a series of plots to find relationships within the data. When he found a trend, he quantified the likelihood that the trend was not due to chance by using frequent statistics. Using the data models he identified, Paul built a supervised machine learning algorithm to predict whether an individual would likely default on their loan. Because of his work, Paul landed a job as data engineer at Synthego before he even graduated from Springboard.

    You can read more about the project here, or view it on GitHub.

  • Using predictive analytics to improve sales outcomes. Now a data scientist at Livongo, delivering key data insights to boost the company’s growth marketing efforts, Springboard alum Mikiko Bazeley decided to build a predictive model for the organization she was working for at the time. She used XGBoost, an open-source software library for applied machine learning, to classify the qualification of sales demo calls in order to improve pipeline forecasting.

    "The capstone is a demonstration of the value that seemingly low-hanging fruit data science projects can have on business organizations like revenue operations," Bazeley said. "This is the kind of collaboration that can happen between data scientists and business partners."

    You can read more about the project here, or view it on GitHub.

What Do You Need to Study to Become a Data Scientist?

Data scientists tend to be well trained in quantitative techniques, given the steep learning curve required to master such a wide range of skills. The most common fields of study are mathematics and statistics followed by computer science and engineering.

However, you can still become a data scientist even if you didn’t study one of these fields. In fact, in an analysis of thousands of data scientist resumes, Indeed found that data scientists had the widest variety of educational backgrounds compared with data engineers, data analysts, and software engineers. Only about 20% studied computer science, and another 20% earned a degree in business or economics. The rest came from math/stats, natural sciences, engineering, data science or other majors.

data scientist job titles

What Are the Key Responsibilities of a Data Scientist?

Data scientist roles require you to perform a number of key functions in different areas. Here are six of the most common responsibilities of a data scientist.

Learn more about the data science process here.

  • Problem definition. The problem statement is the most crucial step towards solving any data analytics problem. Data scientists need to think of the problem statement in mathematical terms. For example, “Why is product X underperforming?” and “What data on user behavior might help explain it?”
  • Data collection and wrangling. Data wrangling is the process of cleaning, restructuring, and enriching raw data to make it analyzable. The primary goal of data wrangling is to reveal a ‘deeper intelligence’ by gathering data from multiple sources and organizing the data for a broader analysis.
  • Exploratory analysis. Exploratory data analysis postpones any initial assumptions, hypotheses, or data models; instead, data scientists seek to uncover the underlying structure of the data, extract important variables, and detect outliers and anomalies.
  • Data processing. The data processing cycle refers to the set of operations used to transform the data into useful information. In this stage, the data is entered into a system, such as a CRM like Salesforce or a data warehouse like Redshift so that a data processing cycle can be established. Next, this process is deployed as a repeatable data model to enable long-term data analytics projects.
  • Model training and deployment. Data modeling represents the way data flows through a software application or the data architecture within an enterprise. It’s almost like a blueprint that establishes relationships between different business entities to show how data is collected and stored.
  • Documentation, visualization, and presentation. Data scientists are expected to document their processes, providing sufficient descriptive information about their data for their own use as well as their colleagues and other data scientists in the future. Visualization is perhaps the most crucial aspect of the data function since computational statistics are only meaningful if they can be understood and acted upon by the organization.

Learn more about the essential responsibilities of a data scientist here.

5 Common Data Scientist Job Roles

Data scientist roles are becoming increasingly specialized as sophisticated software automation takes over tasks like cleaning data and finding redundancies. Different types of data scientist roles focus on a specific part of the data processing cycle, from analyzing data to building the underlying data warehousing infrastructure.

  • Data scientist. Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. They analyze the data and share these insights with the right teams in the form of reports, dashboards, graphs, and charts.
  • Data analyst. Data analysts use business intelligence and analytics tools to sift through the data and identify trends. Their work is focused on generating prescriptive insights. By analyzing historical patterns, they can make data-driven recommendations to optimize future business outcomes.
  • Data engineer. Data engineers are responsible for creating and maintaining the analytics infrastructure that enables almost every other data function. This includes architecture like databases, servers, and large-scale processing systems.
  • Business intelligence specialist. Business intelligence (BI) specialists design and implement BI software and systems, while integrating them with databases and data warehouses. BI gives a readout on the current state of the business by tracking key operations metrics in real-time, providing the business with descriptive, rather than predictive, data.
  • Data architect. Data architects work in tandem with data engineers to conceptualize, visualize, and build enterprise data management systems (DBMS). DBMS refers to the technology solutions used to manage the storage and retrieval of data from databases.

data scientist job roles

Learn more about key data scientist roles here.

Is data science the right career for you?

Springboard offers a comprehensive data science bootcamp. You’ll work with a one-on-one mentor to learn about data science, data wrangling, machine learning, and Python—and finish it all off with a portfolio-worthy capstone project.

Check out Springboard’s Data Science Career Track to see if you qualify.

Not quite ready to dive into a data science bootcamp?

Springboard now offers a Data Science Prep Course, where you can learn the foundational coding and statistics skills needed to start your career in data science.

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