By Samuel Miller
Aptly living up to its hype, "data scientist" continually tops the list of best careers for overall job satisfaction, salary potential, and open job opportunities. Its US median base salary of $120,000 doesn’t hurt either.
Data scientists work in virtually every industry and remain at the forefront of large-scale decision-making and innovation. Healthcare, pharmaceuticals, telecommunications, finance, and energy are just some sectors actively hiring data scientists right now.
This article clarifies some of data science’s elusive mystique, without losing its exciting allure! Learn the main job roles that data scientists specialize in, and what data scientists do across various industries.
What is Data Science?
Before getting into exactly what data scientists do, let’s take a step back to explore data science as a whole.
Basically speaking, data science is the act of programmatically uncovering patterns and meaningful information from large data sets, often with the goal of informing business decisions. Data science is typically applied at the scale of big data, which refers to complex data sets that are too massive to be analyzed by human-driven techniques.
Harnessing the power of data is revolutionizing every sector of our economy. Where there’s data, there’s potential! Think of it as creative analytics. If organizations can gather a plethora of facts, or data points, they can try to understand why you behaved in a certain way. Using data science, organizations can adjust their products or services to better serve you. Or decide the optimal amount of resources to invest in developing a new initiative. They can even attempt to predict or influence your future behavior.
What is data science used for?
Some common data science applications include:
- Detect fraud
- Recommend what we watch and buy
- Help companies identify and address their customers’ needs
- Analyze and tackle global challenges like cybersecurity, public health and safety, and climate change.
Organizations need someone brave enough to dive into the data and bring meaning to the madness. In these scenarios, they employ a data scientist.
What is a Data Scientist?
Data scientists use computer programming skills, logic, and domain knowledge to derive insight and value from data. With a combination of technical skills and curiosity, data scientists identify the potential use of data, ask the right questions, and construct a narrative for the project stakeholders.
Data scientists engage in both descriptive and predictive analytics. With descriptive analytics, data scientists can summarize patterns and information from current and historical data sets. With predictive analytics, data scientists use statistics and modeling techniques to forecast future outcomes.
Who makes a good data scientist?
Technical skills can be learned, but attitude is inherent. There are certain personality traits that lend well to data science. If you’re thinking about getting into data science, see if these characteristics resonate with you:
- You are endlessly curious
- You like to design experiments to test theories
- You are not afraid of complex or messy data
- You are intrigued by mathematical and statistical analysis
- You are adept at – or interested in learning – programming languages like Python, Java, or R
- You see the potential for patterns and trends within data
- You have a knack for storytelling and can render the complex simple
- You communicate effectively in group settings
- You enjoy collaborating with others on projects, and are also content working solo
Few of us can answer yes to all of the above in the beginning. But as the adage goes, “you’ll never learn much if you already know too much.” So if you’re still intrigued thus far, perhaps you would make a good data scientist!
What does a Data Scientist do?
More than just a job title or singular path, data scientists use insight derived from data to address an ever-changing landscape of organizational challenges. Many of today’s jobs work with data and can technically fall under the umbrella of "data science." Depending on the organization or task, a data scientist may combine several overlapping data science roles.
Below is a snapshot of the main job roles that data scientists do. In this section we will discuss a general overview of each role, while remembering that different organizations may have varying definitions of these job descriptions within their particular sector. We also include salary data for each role from Glassdoor.
- Data scientist
- Data analyst
- Data engineer
- Machine learning engineer
Data Scientist
While "data scientist" can function as an umbrella term for the rest of the positions covered below, you’ll also see it listed as its own distinct role on job sites.
As detailed before, data scientists use computer programming, statistics, and logic to gather and analyze data, and aid in making business decisions. Data scientists also utilize written and verbal skills and domain knowledge to communicate their hypotheses, processes, and findings to project stakeholders.
Data Scientist Job Snapshot
- Median Base Salary: $120,000
- Expertise: Predictive + Descriptive Analytics
What does a Data Scientist Do: Day-to-Day
- Considers business problems and questions that might be answered with data
- Runs experiments to discover patterns in data
- Presents insight and recommendations gained from results
- Utilizes programming or querying languages such as Python, R, or SQL
- Some roles utilize mathematical disciplines like statistics, calculus, and linear algebra
- Leads the data team and communicates with senior executives
Data Analyst
A job as a data analyst can be a terrific first step in pursuing a data science career. Data analyst jobs typically require less technical programming experience than data scientist jobs, and working as a data analyst gives you the opportunity to understand data from the ground level.
Data analysts apply descriptive statistics and data visualization techniques to turn raw data into actionable insight. All data scientists engage in data analysis. In comparing a data scientist vs. data analyst: a data scientist hypothesizes new ways to gather, interpret, and act upon data, while a data analyst specializes in cleaning, organizing, and analyzing data that already exists.
Companies in virtually every industry rely on data analysts to derive insight and answer important business questions. A finance firm, for example, might task a data analyst with sifting through stock market data to identify promising trends and opportunities for investors.
Data Analyst Job Snapshot
- Median Base Salary: $74,224
- Expertise: Descriptive Analytics
Data Analyst: Day-to-Day
- Analyzes data to find answers to business questions
- Organizes findings into reports that others can use to make decisions
- Designs meaningful data visualizations
- Works with tools like SQL, Tableau, and of course, good ol’ excel
- Usually deals with much smaller datasets than a data scientist
Data Engineer
Data engineers build and maintain systems for accumulating, transferring, and storing data. Data engineers’ expertise lies in database architecture and the pipelines that supply it. Where there’s no data, there’s no data science. So data engineering plays a crucial role in what data scientists do!
Data engineers gravitate more towards computer programming than their statistical counterparts in data science and data analysis. Currently, data engineering constitutes a relatively newer specialization in the world of data scientists and is experiencing tremendous growth.
Data Engineer Job Snapshot
- Median Base Salary: $113,000
- Expertise: Programming and Data Infrastructure
Data Engineer: Day-to-Day
- Designs the systems for accumulating and organizing data
- Creates data pipelines and infrastructure
- Prepares data in a format that data scientists and analysts can utilize
- Responsible for storing, streaming, and processing data
- Utilizes programming languages such as Python or Java
Machine Learning Engineer
A machine learning (ML) engineer develops computer models that learn from historical data to find patterns or predict future outcomes.
A subdiscipline of artificial intelligence (AI), ML represents one of the most sought-after data science specialties, cementing itself as a foundational skill for data scientists hoping to work at the forefront of the field. Because machine learning engineers require the most technical expertise, they also command the highest median base salaries of the four data science job roles discussed in this article.
Machine Learning Engineer Job Snapshot
- Median Base Salary: $130,000
- Expertise: Predictive Analytics, Programming, Statistics
Machine Learning Engineer: day-to-day
- Uses algorithms to build models that learn from, often large quantities of data
- Explores ways to improve product recommendations, marketing strategies, customer service, company profits, and more
- May work in various specializations such as image recognition, natural language processing, voice recognition, geographic information systems (GIS), self-driving vehicles, and more
Now that we know about some of the specialized roles data scientists perform, let’s look into some of the industries hiring them the most.
SEE ALSO: If you’re interested in becoming a machine learning engineer, you can start a free trial of CodingNomads Data Science and Machine Learning Career Track – Ranked the #3 Data Science Program by SwitchUp.
Industries that Need Data Scientists
Virtually every industry relies on data to make decisions, including across corporate, non-profit, educational, and governmental entities. As organizations increasingly use data-driven approaches to meet their needs, the demand for data scientists continues to grow.
This boom shows no signs of abating, with the Bureau of Labor Statistics (BLS) projecting a 22% growth in “Computer and Information Research Scientist” jobs between 2018- 2028.
Below are the top 4 industries that expect the most growth in data science jobs, and what data scientists do in each industry:
- Healthcare and Pharmaceuticals
- Telecommunications
- Banking/Financial Services
- Energy
Healthcare and Pharmaceuticals
Healthcare and pharmaceutical organizations employ data science teams to tackle a host of needs – from clinical trial analysis, electronic medical records, imaging services, and more. Data science in the healthcare industry directly informs effective care practices, research & development, and policy writing.
What does a data scientist do in healthcare?
- Pharmaceutical studies
- Disease diagnostics
- Quality improvement projects
- Health systems optimization
- Health risk assessment
The healthcare analytics market is predicted to grow by 26% through 2027. Even more substantial, the data engineering/architecture arena within healthcare analytics is expected to grow by 48% through 2027. Predictive analytics, as in machine learning, will also see considerable growth as companies seek to cut administrative costs and improve services.
Telecommunications
Every day we generate immense amounts of data through our phones and apps, making data central to the telecommunications industry. Telecom companies task data scientists with anything from improving customer experience to network security, making it an excellent industry for various skill sets.
What do data scientists do in telecom?
Data scientists in telecom use data analysis and machine learning methods to help businesses improve:
- Strategic Marketing
- Network security
- Fraud detection
- Product optimization
- Behavior prediction
- User experience
With the tremendous amount of data pouring in from calls, texts, and mobile apps, the telecom analytics market is expected to grow by 14.51% between 2022 and 2026.
Banking/Financial Services
Often called “Fintech,” the banking and financial services industry leads the world in adopting data science technologies. For example, machine learning and AI can automate stock market analysis, and provide investors with actionable insight in real-time. Fintech companies also employ machine learning engineers to build forecasting models that assist in financial planning and portfolio management.
What do data scientists do in finance?
- Fraud detection
- Risk modeling
- Loan appraisal management
Machine learning / AI in the Fintech market is expected to grow by 23.17% between 2021 and 2026.
Energy
With the growing demand for electricity, the energy sector depends on data science to continually innovate in the face of new challenges. Data scientists can help companies predict outages and equipment failure, test new technologies, improve customer service, and more.
Data science is poised to play a pivotal role in further developing and maintaining and improving the efficiency of the power grid. For example, machine learning can help determine the best locations for renewable energy infrastructure, as well as manage grid capacity during peak usage.
What do data scientists do in the energy sector?
- Predict potential outages, surges, and other power supply issues
- Determine optimal sites for renewable energy infrastructure
- Model research of emerging technologies
- Improve customer relations
Especially as we transition into a “smarter,” data-driven power infrastructure, data scientists skilled in machine learning and analytics are increasingly in-demand in the energy sector. According to Schroders energy sector market analysis, transitioning the grid will cost more than $120 trillion by 2050 and require significant market disruption and technological innovation. A data scientist’s playground!
Salary for a data scientist
With high demand often comes higher compensation – and data science is no exception. The salary for a data scientist can vary according to several factors, like industry and location. For a general idea in the United States, the graph below depicts the median US base salary for a data scientist by job role and experience level, according to data from Glassdoor.
Recap
This article answered some primary questions about data scientists including:
- What is data science > Data science is the act of programmatically uncovering patterns and meaningful information from large data sets, often with the goal of informing business decisions.
- What does a data scientist do > Data scientists use computer programming skills, logic, and domain knowledge to derive insight and value from data.
- Who makes a good data scientist > Technical skills can be learned, but attitude is inherent. Curiosity is key!
We also discussed some primary job roles for what data scientists do on the day-to-day, including:
- Data Scientist (general)
- Data Analyst
- Data Engineer
- Machine Learning Engineer
Some of the largest industries hiring data scientists today include:
- Healthcare and Pharmaceuticals
- Telecommunications
- Fintech
- Energy
It Doesn’t Stop Here!
The world of data science continues to evolve as more and more industries adopt it in their business strategies. If you want to discover more about this fascinating field, check out our Data Science & Machine Learning Course, which is ranked the #3 Data Science Program by SwitchUp.