Data science is a broad, many-sided discipline. Dr. Ganapathi Pulipaka, Chief Data Scientist at Accenture, asserts that data science is an amalgamation of fields that utilize “software engineering, predictive analytics, machine learning, deep learning, HPC, supercomputing, mathematics, data mining, databases (SQL, NoSQL), Hadoop, streaming analytics platforms for live analysis (Apache Kafka, Apache Flink, Apache Spark, Apache Impala), IoT platforms, edge computing, fog computing, networks, statistics, web development, cloud computing, data engineering, and data visualization.”
It’s complex because it has to be. Businesses, organizations, and governments could once expose the insights hidden in data using spreadsheets and relatively simple statistical methods. Today, humanity generates 7.5 sextillion gigabytes of digital data daily. Separating the signal from the noise to find the insights buried in all that information is a lot more complicated. Data scientists must be experts in the techniques and tools listed above, as well as domain experts and increasingly, automation experts. They also tend to have advanced degrees and significant professional experience.
Consequently, the average master’s in data science salary is much higher than the national average across all occupations. Data scientists across the United States earn a lot—typically over $100,000—because organizations rely on them to solve abstract problems, predict future events, and boost profits. That figure doesn’t represent a cap, however. How much you can earn with a master’s in data science will depend on your professional and academic background, your title, and other factors explored in more detail below.
The career benefits of earning an MSDS are numerous and transcend the wage premium associated with this degree.
Why earn a Master of Science in Data Science (MSDS)?
The career benefits of earning an MSDS are numerous and transcend the wage premium associated with this degree. According to the U.S. Bureau of Labor Statistics (BLS), U.S. employment in data science will grow by 31 percent in the coming decade, which means employers will create thousands of new jobs for data scientists. What you need to know is that landing one of those newly created positions will likely mean earning a master’s degree first. Surveys conducted by executive recruitment firm Burtch Works show that 90 percent of working data scientists have advanced degrees and nearly half of all data science job postings require that applicants have master’s degrees in addition to experience.
Fortunately, pursuing a graduate degree to gain access to opportunity and earn a higher-than-average master’s in data science salary doesn’t have to mean sacrificing income, professional advancement, or personal time. You don’t have to leave the workforce to enroll in Tufts School of Engineering‘s online Master of Science in Data Science program. In fact, you can pursue an MSDS while continuing to work full-time. Because Tufts delivers the 10-course, 32-credit hour data science master’s entirely online in a flexible, part-time format, you can take the same core courses taught by the same faculty members as students who attend in-person classes at the university’s Boston-area campus. But unlike students who take time off to go to graduate school, you can apply the data mining, data infrastructure, data analysis, and machine learning skills you learn in the classroom right away in your current role.
How much do data scientists earn?
Data science salaries tend to be high because most data scientists have advanced training and diverse skills, but determining what the average data scientist earns is challenging. Indeed reports that data science professionals in the U.S. earn about $119,000 while PayScale’s reported average is closer to $97,000, and most mid-career data scientists probably earn salaries that fall somewhere in between.
Gauging the impact of a master’s degree on data science salaries is similarly challenging. Because master’s degrees and Ph.D.s are common in the field, there isn’t a lot of accurate information about how much data scientists with bachelor’s degrees earn. Salary.com reports that the typical data scientist with a bachelor’s degree earns between $82,000 and $88,000 but also acknowledges that those figures are for early-career data science professionals.
What is abundantly clear is that data scientists in managerial roles and data scientists who work at high-profile technology companies can earn close to $200,000, if not more.
What are the highest-paying jobs with an MSDS?
There is no consensus regarding which data science positions pay the most. Sites such as Glassdoor and other salary aggregators rely on self-reported salary figures to calculate their averages for the various positions in data science. Further complicating matters is the fact that titles in data science are usually created by hiring managers, not data science professionals. For instance, one company’s data scientist might be another company’s data engineer, so there’s a lot of overlap in the top-paying titles in data science.
Chief Data Scientist
Average salary: $176,000 Sometimes known as a Chief Data Officer, this c-suite executive oversees the management, storage, and analysis of data across an organization. They are responsible for driving data science strategy and guiding how the data scientists in their organizations leverage data to inform business decisions.
Average salary: $121,000 These data science professionals design (and sometimes build) the underlying structure of complex data frameworks based on business or organizational requirements. Data architects collaborate closely with data engineers to visualize and create the “blueprints” used to build data management and exploration systems.
Data Science Manager
Average salary: $137,000 Data science managers help their organization leverage Big Data by coordinating the efforts of different members of the data science team. They work with data scientists, data engineers, data architects, data modelers, and other professionals in the field to ensure that everyone is working toward the same goals.
Data Warehouse Manager
Average salary: $124,000 Data warehouse managers oversee the collection, storage, and use of data. They are experts in how people access and interact with data repositories and the information contained therein. Their number one priority is ensuring data is stored and structured in a way that serves the organizations they work for, but some data warehouse managers are also responsible for keeping data secure.
Director of Data Science
Average salary: $155,000 The director of data science guides the activities of the data science team and serves as a bridge between business stakeholders and the department. Some professionals in this role primarily manage people and projects while others are responsible for developing new data-driven approaches to organizational challenges or streamlining data acquisition and analysis.
Head of Data Science
Average salary: $176,000 The head of data science may have responsibilities similar to those of the data science director or do work entirely focused on the creation of new data science capabilities. At some companies, this may be a highly technical role or a business-facing role.
Senior Big Data Engineer
Average salary: $129,000 Big data engineers process vast quantities of data using various technologies. Unlike data scientists, data engineers usually develop and maintain the software and hardware architecture and processes organizations use to gather structured data, unstructured data, and semi-structured data. Their role is to create stable pipelines that deliver data in ready-to-analyze formats so data scientists can interpret and report on that data.
An MSDS from Tufts School of Engineering signals to potential employers that you have the high-level technical and soft skills necessary to move the industry forward.
Senior Machine Learning Engineer
Average salary: $156,000 Machine learning engineers in data science design and productionize predictive models to ensure the largest possible information gain. Senior machine learning engineers oversee the machine learning activities that support data science initiatives. Of all the high-paying jobs for data scientists, this one is the most technical and usually involves the most programming.
Unsurprisingly, many data scientists work in technology. According to Diffbot’s State of Data Science, Engineering & AI Report, the companies with the largest data-related workforces include Amazon, Apple, Facebook, Google, IBM, and Microsoft—all of which are known for paying employees with leading-edge technology skills very well. The highest-earning data scientists, however, work for Airbnb, Dropbox, Lyft, Netflix, Pinterest, Twitter, and Uber. Data scientists at these companies typically earn more than $200,000, regardless of title.
Becoming a data scientist doesn’t have to mean working in tech, however. An MSDS can open doors for data scientists in retail, banking and finance, Big Pharma, healthcare, entertainment, and other industries. Jobs for data scientists outside of technology may not pay as well as positions at the FAANG firms, but they can still pay quite a lot.
What other factors affect salaries in data science?
Education isn’t the only factor that influences data science salaries. The reason salary averages reported on sites such as PayScale and Monster.com vary so widely is that those averages are calculated using figures submitted by data scientists with different levels of experience working in different areas of the country. Indeed, for instance, reports that data scientists earn the highest salaries in California, Connecticut, Delaware, New York, and New Mexico. Specialty can also affect pay, which is another reason there are $70,000 data scientist jobs and $200,000+ data scientist jobs. Data professionals who work in computer manufacturing, finance, and banking tend to earn the most, according to the U.S. Bureau of Labor Statistics.
The benefits of attending a respected MSDS program
Reputation matters when it comes to data science master’s degrees. An MSDS from Tufts School of Engineering signals to potential employers that you have the high-level technical and soft skills necessary to move the industry forward. The value of a Tufts data science master’s transcends name recognition, however. Students in the MSDS program receive tailored guidance from advisers who are leaders in their fields and are committed to helping students reach their full potential. The School of Engineering maintains a Piazza board where faculty post research opportunities, internship opportunities, and information about scholarships, hackathons, career fairs, and opportunities for professional development. And Tufts’ online students benefit from the school’s proximity to and connections with an array of tech firms and startups across industries in the Greater Boston region, even as they study virtually. Graduates of the online MS in Data Science program also benefit from membership in the elite Tufts University alumni network, which includes more than 100,000 graduates from around the world.
There’s also the benefit of studying with experienced professionals. Students in the online data science master’s program tend to be data scientists, senior software engineers, and product managers. Many already work in technology-focused leadership roles. They’re experts in their fields, highly accomplished, and familiar with the topics in the interdisciplinary MSDS curriculum (e.g., data analytics, probabilistic systems analysis, algorithms, machine learning, data mining, and Big Data). Distance learners enrolled in Tufts’ program benefit from the achievements of their peers as they tackle demanding data science challenges together.
Can choosing an online MSDS program further boost your earning potential?
The average master’s in data science salary doesn’t change based on how professionals earn their degrees, but MSDS candidates who choose online programs may graduate one step ahead of peers in on-campus programs. The flexibility of Tufts’ 100 percent online MSDS program lets professionals enroll in a highly ranked data science graduate program without putting career advancement on hold. They don’t have to sacrifice income in the short term to earn a Master of Science in Data Science or settle for a lower-quality program that’s within commuting distance.
Most importantly, they can apply what they learn immediately and increase their lifetime earning potential in the process. Some MSDS candidates transition into higher-paying data science jobs before graduation. Others earn raises or promotions while studying because they can leverage the power of data science in their current roles. In both cases, students graduate earning more than they would have otherwise and their later-career salaries are higher as a result of this early-career increase.
Should I pursue a career in data science?
To determine whether a career in data science is right for you, consider your motivations first. While the average master’s in data science salary is certainly compelling, the bar data scientists must exceed to transition into high-paying jobs has never been higher. Data scientists earn base salaries up to 36 percent higher than other analytics professionals because they bring advanced skills to the table and can “exploit data regardless of its source, size, or format.” It’s not enough to have a head for statistics and programming chops, however. You need to be passionate about unlocking the potential for data—particularly in your domain. Data science is as much about determining which questions to ask as it is about finding answers.
From there, ask yourself whether the notion of lifelong learning is an appealing one. Early data scientists often earned big bucks for simply slicing and dicing data. Today’s data scientists have to deliver quantifiable value with tools unlike anything their predecessors used. Tomorrow’s data scientists will likely leverage techniques and tools very different from those in use today. To stay relevant for the duration of your career, you will need to update your skills and knowledge as computer science, data science, and business intelligence evolve.
Finally, consider whether you are ready to invest in the education necessary to become a data scientist. There was a time in which aspiring data scientists could break into the field with a bachelor’s degree in statistics, a BS in Computer Science, or even a boot camp certificate, but the Burtch Works findings above demonstrate that is no longer the case. Employers expect applicants for data science jobs to have master’s degrees, and most do. To compete—and to join the ranks of the highest-earning data science professionals—you’ll need a degree from a top-ranked MSDS program offered by a school with a reputation for engineering excellence.
Tufts’ part-time, interdisciplinary master’s-level data science curriculum trains agile data scientists who can apply today’s data analysis methodologies across various enterprises and adapt as technology changes. The leading-edge curriculum takes under two years to complete, and graduates emerge ready to tackle real-world challenges and make a difference with data—now and in the future. If you’re ready to join them, apply now. If you still have questions, sign up for an upcoming enrollment event or read more about the program’s admission requirements, the student experience at Tufts, and what you can do with a master’s in data science.