What Does a Data Scientist Do and How Much Do They Earn?

Demand for data scientists is rising faster than for most other roles today—but what does a data scientist do? These scientists use analytics, domain knowledge, programming skills, and modern tools to develop actionable insights from information collected from various sources.¹ These insights and skills are in demand across virtually every industry as businesses continue to embrace new technology and analytically driven strategies.²

This post explores data scientists' job descriptions, key responsibilities, essential skills, and career outlooks.

Key Responsibilities

What does a data scientist do on a daily basis? While responsibilities vary based on the specific role and industry, most data scientists share the same core responsibilities:

• Data wrangling and preprocessing: Clean and structure raw data to support analysis and modeling processes
• Statistical modeling and machine learning (ML): Build and use advanced models and algorithms to interpret complex data, leveraging pattern recognition, predictive analytics, and artificial intelligence (AI)
• Experiment design and A/B testing: Create controlled tests, such as A/B tests that compares two inputs' outcomes, to track positive and negative changes in data
• Data visualization and reporting: Share complex information using charts, graphs, and other visuals to highlight significant changes and make the information more digestible to others
• Model deployment into production: Rather than focusing solely on internal insights, data scientists also design ML models with real-world uses for end-users, such as AI-powered software features

Essential Skills for Data Scientists

While data scientists share many of the same skills and proficiencies, specific skills and specializations will vary by the fields and roles in which they work. For example, the particular data tools and programming languages you need to learn often vary by industry.

Essential data science skills you'd see in a data scientist job description:

• Programming languages: Data scientists should be proficient in programming languages related to their field, such as Python, R, Structured Query Language (SQL), Java, and C++
• Machine learning and statistical knowledge: Data scientists should know the functions of and how to use complex algorithms, statistical models, data frameworks, and experimental designs
• Data visualization tools: Data scientists must also be able to use relevant business intelligence (BI) platforms and programming libraries, such as Seaborn, Tableau, and Power BI, to support data visualization
• Cloud platforms and big data technologies: Similarly, data scientists must be proficient with appropriate storage platforms, tools, and databases, such as Microsoft Azure, Apache Spark, and Vertex AI
• Soft skills: To be effective workers and team members, data scientists should also be skilled in communication, problem-solving, collaboration, adaptability, and critical thinking

Where Data Scientists Work

What a data scientist does day to day varies by place of employment. Data scientists fill roles in several industries and types of organizations:

• Technology companies: Data scientists working in tech analyze information using internal models and strategies to continuously improve products, operations, and customer experiences
• Healthcare and biotech: Data scientists in healthcare offer ML insights and data analyses to predict patient risks and diseases, suggest treatments, and support data-driven decision-making³
• Finance and fintech: Data scientists in finance build models to detect fraud, manage risks, improve credit scoring processes, elevate customer experiences, and analyze trading market information⁴ 
• Retail and e-commerce: Data scientists help retail and e-commerce companies understand their customers by using data analytics to track behavior, predict trends, and highlight strategies to optimize sales
• Government and research organizations: Data scientists in civil services and research manage vast amounts of information from public data sets to provide more accurate insights for policies and services⁵

Salary Expectations

In the United States, the average salary for data scientists in 2024 was nearly $113,000, equating to about $54 per hour.¹ However, the exact numbers vary by industry, location, role, and seniority, with annual data scientist salaries ranging from around $63,000 to $194,000.⁶ Entry-level roles tend to fall on the lower end of that range, while senior roles typically pay more.

Estimated 2024 data scientist salaries by specialization include:⁶

• Computer systems design: $128,000
• Company or enterprise management: $127,000
• Scientific research and development: $120,000
• Management, scientific, and technical consulting services: $110,000
• Insurance carriers: $109,000

Advanced degrees and certifications can meaningfully increase your salary potential by qualifying you for more specialized roles. For instance, demonstrating expertise in machine learning and AI on your resume can make your application stand out, as demand for these skills continues to grow across industries.

Career Outlook

According to the U.S. Bureau of Labor Statistics (BLS), data scientists have an extraordinarily high job outlook, with a projected growth of 34% between 2024 and 3034. About 23,400 new data science jobs are projected to open each year, on average, with most of these roles being predominantly full-time in the office.¹

Much of the demand for data scientists is driven by increased reliance on AI, ML, deep learning, generative models, and other emerging technologies that require training and experience to work with.⁷ Additionally, more positions are expected to open as current workers retire or switch industries.¹

Launch Your Career as a Data Scientist

Data scientists' roles and responsibilities can vary across industries, but they often share the same common objectives: managing and analyzing information and complex models to improve data accuracy, tech operations, and customer experiences. Data science remains a promising career choice as job demand continues to skyrocket and the development of new techniques and specialties diversifies where your career can take you.

New York Tech's Online Data Science, M.S. program offers specialized courses taught by expert faculty. Combined with industry networking opportunities, New York Tech can supply you with the skills to grow your career long-term, including working in financial technology (FinTech) and working with ML and other new data science technologies.

The 100% online program offers three potential start dates per year and can be completed in as few as three months. The Online Data Science, M.S. program doesn't require course prerequisites or technical background for admission. This flexibility can make it easier for you to elevate your data science career while still managing your other life responsibilities.

Explore the Online Data Science, M.S. curriculum and course info, and schedule a call with an admissions outreach advisor to get started.