Data science lies at the intersection of statistics and machine learning, the technology that powers artificial intelligence. As Forbes magazine explains, “[i]f data is the oil of the information age and ML is the engine, then data science is the digital domain’s equivalent of the laws of physics that cause combustion to occur and pistons to move.”
The demand for data scientists is growing. The U.S. Bureau of Labor Statistics (BLS) projects a growth rate of 22% for data scientist jobs between 2022 and 2030. Data science salaries are on the rise as well. A 2021 survey by the Institute of Electrical and Electronics engineers reported that median salaries for data scientists rose 8% in a single year.
In this blog post, we’ll explore data scientist salary differences across various data science roles and discuss how industry, experience, and location can affect compensation.
What Does a Data Scientist Do?
Data scientists extract business intelligence from unprocessed data generated by IoT devices, web traffic, social media channels, and other digital sources. They use a combination of domain knowledge and technical expertise to identify business problems that can be solved with data-driven insights. Data scientists then wrangle and prepare data so that it can be analyzed. Then, data scientists use inferential statistics and machine learning models to identify relevant trends and extract valuable insights from data. They synthesize their findings for key stakeholders with data visualizations and data storytelling and use these tools to communicate data-driven business recommendations.
How Much Do Data Scientists Make?
Data scientists earn an average annual salary of $100,560, according to the U.S. Bureau of Labor Statistics (BLS). However, data science salaries vary by role, industry, experience, education, and more.
Data Scientist Salaries by Role
“I think of data science as an amalgam term,” says data scientist/machine learning engineer and Springboard alum Sam Fisher. “When I hear the term, ‘data science,’ I think about how the fields of statistics and machine learning are mixing.”
Data science is an interdisciplinary field that encompasses a variety of subdomains, including data analysis, data engineering, machine learning, and deep learning. Let’s explore the responsibilities and average compensation statistics associated with different data science roles.
Data Scientist
Data scientists use statistics, machine learning, and data mining techniques to solve business problems. They also apply a combination of industry knowledge and technical expertise to identify organizational pain points and translate these issues into data science questions.
(Job posting, Data Scientist at builtin.com, 2022)
As this job post describes, data scientists at Veeva—a company that builds cloud-based software solutions for the life sciences industry—use experience with prescription and medical claims data to better understand the organization’s data science objectives.
Data Engineer
Data engineers earn an average base salary of $116,311 according to Indeed.
Data engineers design data management systems and maintain analytics infrastructures. The analytics objectives of the data science team determine a data engineer’s design decisions.
(Job posting, Data engineer at Hewlett Packard, 2022)
As this job post emphasizes, data engineers apply industry knowledge to create data infrastructures that solve business problems.
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Machine Learning Scientist
Machine learning scientists earn an average salary of $137,053, according to Glassdoor.
Machine learning scientists research, develop, and implement machine learning solutions. They study emerging ML and AI technologies and evaluate their potential business applications. Machine learning scientists also identify business problems that data-driven solutions can address.
(Job posting, Machine learning scientist at Amazon, 2022)
Data Analyst
The average base salary for data analysts is $69,517 according to Glassdoor.
Data science and data analytics are interconnected fields. Data analysts collect, prepare, and analyze data, then organize their findings into visualizations and narratives that inform business strategy. While data scientists typically formulate their own data questions, data analysts often answer questions determined by business stakeholders. Data analysts produce actionable business insights that create competitive advantages and guide decision-making processes. They also collaborate with stakeholders to determine what questions should be addressed.
(Job posting, Data analyst at FedEx, 2022)
Deep Learning Specialist
The average salary for deep learning professionals is $137,941, according to ZipRecruiter.
Deep learning is a subset of machine learning that enables systems to learn and make predictions from large amounts of data. It powers many AI applications related to automation.
Deep learning specialists help develop AI research concepts into scalable, robust systems. They study and implement emerging AI solutions and build models to analyze large volumes of unstructured data. They also use data storytelling techniques to communicate insights to business stakeholders.
(Job posting, Senior Deep Learning Engineer at BioTrillion, 2022)
This role focuses specifically on computer vision, a type of AI-powered by deep learning. This company applies computer vision techniques to analyze healthcare image data in ways that improve health outcomes.
Data Scientist Salaries by Industry
Data scientist salaries can vary widely across industries, so let’s take a look at how these salaries can differ.
Tech
According to the U.S. Bureau of Labor Statistics, data scientists working in computer systems, design, and related services earn $111,490 on average, while data scientists working in software publishing earn $115,110 on average. Data scientists working in information services earn $121,410, and data scientists working in computer and peripheral equipment manufacturing earn $144,090 on average.
In the tech industry, data drives product development. Data scientists in tech also use data to improve and evaluate products and services. And, they help tech companies monetize data.
Meta, for example, monetizes large volumes of user data through targeted advertising. This position at Meta applies data science to measure and communicate effective strategies for online advertising.
(Job posting, Data scientist at Meta, 2022)
Healthcare
The average annual data scientist salary working in the healthcare industry is $87,530, according to the U.S. Bureau of Labor Statistics. (This figure does not include data science salaries from health insurance corporations, which the BLS categorizes as part of the financial industry).
The healthcare industry uses data science to improve care, predict patient risk, develop treatments, and guide clinical interventions. Data scientists in the healthcare industry also optimize hospital efficiency, improve resource allocation, and map community health trends.
(Job posting, Healthcare data scientist University of Maryland Medical System, 2022)
The University of Maryland Medical System is looking for a data scientist to develop predictive and prescriptive models to support clinical outcomes and the hospital’s operational efficiency.
Government
The average salary for federal, state, and local government data scientists is $71,620, according to the U.S. Bureau of Labor Statistics.
While intelligence organizations like the NSA are well-known for collecting data through mass-surveillance programs, federal agencies like the Department of Agriculture and the Department of Transportation gather data to guide everyday policy decisions. State and local governments are embracing digital transformation as well, and use analytics to improve efficiency, develop social programs, and measure policy success.
(Job posting, Interdisciplinary data scientist at usajobs.gov, 2022)
For this role, the successful candidate will use data science to advance government accountability initiatives.
Finance
Data scientists working in financial services earn an average salary of $109,720, according to the U.S. Bureau of Labor Statistics. Data scientists who work in securities, commodity contracts, and other investment-related activities earn an average annual salary of $143,490.
Data scientists in the financial industry detect fraud, model financial risk, and manage investment portfolios. They also identify ways to improve financial products and solutions.
(Job posting, Data scientist at Enova, 2022)
The successful candidate for this posting will develop, test, and improve models that determine lending criteria and verification procedures.
Ecommerce
The average annual salary for e-commerce data scientists is $122,940 according to the U.S. Bureau of Labor Statistics.
Ecommerce retailers leverage ML-powered recommendation engines to personalize customer offerings and increase sales. They also harness data science to optimize merchandising and pricing, manage inventory, and analyze customer attitudes.
(Job posting, Data scientist eBay, 2022)
This data science job focuses on risk assessment and fraud detection related to Ebay’s payments strategy.
Data Scientist Salaries by Experience
Data science salaries vary according to experience. Let’s explore what you can expect to earn as your career progresses.
Entry-Level Data Scientist Salary
According to PayScale, an entry-level data scientist with less than a year of experience earns $85,075 on average. With one to four years of experience, early-career data scientists can earn $95,996 on average.
As an entry-level data scientist, you might look for roles like this:
(Job posting, Junior data scientist at Roq.ad, 2022)
Mid-level Data Scientist Salary
According to PayScale, mid-level data scientists with four to nine years of experience earn an average salary of $110,727.
As a mid-level data scientist, you might look for roles like this:
(Job posting, Associate data scientist at Soar Management Consulting, 2022)
Senior Data Scientist Salary
According to PayScale, experienced data scientists with 10+ years of experience earn $122,234 on average, and those with 20+ years of experience earn $135,156.
As a senior-level data scientist, you might look for roles like this:
(Job posting, Senior data scientist at McKinsey, 2022)
Data Scientist Salaries by Education
Data from Salary.com shows that education credentials can significantly affect data scientist salaries. Let’s take a look at how data scientist salaries vary according to the level of education.
No Degree
A data scientist with a high school diploma can expect their salary to fall within a range of $82,833-$88,638 according to Salary.com. Although data science jobs typically require a college degree, some positions simply require relevant experience.
(Job listing, Junior data scientist at KesarWeb, 2022)
Bachelor’s Degree
Salary.com reports data scientists with bachelor’s degrees can expect their salary to fall in the range of $83,753 – $89,379.
(Job posting, Associate data scientist at SwissRe, 2022)
Masters
Data scientists who hold master’s degrees can expect their salaries to fall in the range of $84,521 – $90,119 according to Salary.com.
(Job posting, Safety data scientist at ZipRecruiter, 2022)
How To Boost Your Data Scientist Salary
To maximize your data science salary, you can increase your value by mastering in-demand skills, acquiring experience, and earning key credentials. Because data science salaries vary by geographic location, you should also pursue roles in areas where data science compensation is high. Finally, you’ll need a robust negotiation strategy to win the salary you deserve. Read on to find out more about how to boost your data science salary.
Grow Your Skill Set
Machine learning and deep learning knowledge can boost your data science salary. Mastery of programming languages like Python and R is vital, as is fluency with libraries like scikit-learn, TensorFlow, and PyTorch. Proficiency with data visualization tools is also important. You can build these skills through a data science bootcamp program that emphasizes applied learning.
Build Experience
To expand your professional experience, pursue a data science internship. Look for internships that offer collaborative, hands-on opportunities. This will help you build a track record of adding value to a team. An internship will expand your professional network and connect you with job recommendations. A strong job recommendation will validate your skills to potential employers, which can help boost your salary.
Aspiring data scientists can also build experience by completing hands-on data science projects. You can create independent projects using public datasets, enter data science competitions, or enroll in a bootcamp program that offers opportunities to work on real-world data science projects and help you build a professional data science portfolio.
Select the Right Location
According to the BLS, top-paying cities for data scientists in the United States include:
- San Francisco, California. Average salary: $148,680.
- San Jose, California. Average salary: $146,400.
- New York, New York. Average salary: $129,250.
- Durham-Chapel Hill, North Carolina. Average salary: $125,790.
- Seattle, Washington. Average salary: $121,090.
Data scientists in the tech industry are well-compensated, and as a result, the top cities for data science compensation are associated with Silicon Valley. Other high-paying metropolitan areas for data scientists include Boise City, ID, Los Angeles, CA, and Santa Rosa, CA.
Negotiate Your Job Offer
Negotiation skills are key to securing a robust compensation package. Start by researching data science salaries to better understand your market value. Be ready to provide concrete examples of how your skills and experience will be valuable to your employer.
When negotiating a job offer, consider whether you want insurance benefits, along with perks like relocation assistance, paid time off (PTO), and work-from-home accommodations. Be sure to get your compensation package documented in writing and signed by both yourself and your employer. Refrain from accepting job offers on the spot.
Pursue a Certification
According to Sachin Gupta, co-founder and CEO of hiring platform HackerEarth, cloud computing skills add considerable value to a data science resume. A certification from a cloud services provider like Amazon Web Services (AWS) or Google Cloud Platform (GCP) can also help candidates negotiate a higher salary.
Data Science Salary FAQs
Wondering if data science is the right career path for you? Check out these answers to your top questions about data scientist jobs.
Is Data Science a Good Career?
Data science salaries are highly competitive, and data scientists enjoy meaningful, intellectually stimulating work. Because data science is an emergent field, data scientists are constantly learning about new technologies. Data scientists produce impactful solutions that influence organizational strategy, and have extensive opportunities for career advancement. Glassdoor ranked “data scientist” as the number three job in America for 2022.
Are Data Scientists in Demand?
The Bureau of Labor Statistics predicts that there will be a 31.4% growth in employment opportunities for data scientists between 2020 and 2030. Studies show that there’s been hiring acceleration for data scientists, spurred by an expanding need for analytical talent as organizations pursue digital transformation initiatives.
How Much Money Can Data Scientists Make?
Tech companies typically offer the most competitive data science salaries. According to Indeed, top-paying organizations for data scientists include:
- Apple: $180,042.
- Airbnb: $179,69.
- Twitter: $176,922.
- Ebay: $174,291.
Other tech companies like Meta (Facebook), Uber, Lyft, Dropbox, and Netflix often top data science salary lists.
Do You Need a Degree To Be a Data Scientist?
A degree is not required to become a data scientist, but many data scientists hold a degree in math or computer science. Aspiring data scientists can also supplement a degree in an unrelated field with a certificate from Springboard’s Data Science Career Track, a bootcamp program that builds applied data science and machine learning skills (and comes with a job guarantee).
Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.
In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you.
Is Data Science A Good Career?
Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.
10 Data Science Careers To Consider
Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.
Data Scientist
Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.
They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies.
General Requirements
A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills.
Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.
Average Salary
The average salary of a data scientist in the US is $156,363 per year.
Data Analyst
A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.
A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.
General Requirements
To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills.
For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.
Average Salary
The average base salary of a data analyst is $76,892 per year.
Business Analyst
Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation.
A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.
General Requirements
Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.
Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.
Average Salary
A business analyst can earn an average of $84,435 per year.
Database Administrator
The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools.
A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.
General Requirements
Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server.
Average Salary
Database administrators earn an average salary of $77,391 annually.
Data Engineer
Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).
General Requirements
Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently.
Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.
Average Salary
Data engineers earn a whooping average salary of $125,180 per year.
Database Architect
A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure.
Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.
General Requirements
If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java.
If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.
Average Salary
Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.
Machine Learning Engineer
A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions.
General Requirements
As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.
Average Salary
Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.
Quantitative Analyst
Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions.
They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.
General Requirements
This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background.
Average Salary
A quantitative analyst earns an average of $173,307 per year.
Data Mining Specialist
A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.
General Requirements
If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field.
Average Salary
Data mining specialists earn $109,023 per year.
Data Visualisation Engineer
Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context.
General Requirements
Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.
Average Salary
The average annual pay of a data visualization engineer is $103,031.
Resources To Find Data Science Jobs
The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.
Job Boards
When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity.
Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.
Online Communities
Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.
Network And LinkedIn
Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.
FAQs about Data Science Careers
We answer your most frequently asked questions.
Do I Need A Degree For Data Science?
A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.
Does Data Science Need Coding?
Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.
Is Data Science A Lot Of Math?
It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.
What Skills Do You Need To Land an Entry-Level Data Science Position?
To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.
Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!
