Data science is a field that encompasses several different disciplines. But all of these data science roles require strong analytical skills, technical knowledge, and the ability to glean actionable insights from troves of information.
As more and more industries use innovative data applications—from the healthcare sector using data science to improve patient care and drug development to Spotify using music data for personalized recommendations—data professionals of all stripes are in high demand. And there are a plethora of diverse job opportunities that cater to a wide range of passions and interests.
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Why Do Data Scientists Get Paid So Much?
Data science professions command high salaries because there’s not enough supply (the profession has nearly 20,000 open positions in the United States, according to US News) and their skills are in high demand.
Data scientists are seeing this demand because, in the past few years, a growing number of organizations have begun to see the importance of making data-driven decisions. Whether it’s e-commerce companies making forays into artificial intelligence or travel companies incorporating machine learning onto their platforms, data scientists are at the forefront of the latest tech revolution.
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Highest Paying Data Science Jobs
In the United States, data science professionals typically earn around $117,000 a year, according to Glassdoor. However, this can vary depending on several factors, including years of experience, level of education, industry, location, and area of specialization. For example, in major cities such as New York or San Francisco, data scientists can make as much as $180,000. Machine learning engineers often earn more than data analysts, and those who work in the consumer technology sector usually make more than those who work for governmental agencies, nonprofits, or healthcare organizations.
A data scientist identifies data sources, automates data retrieval, preprocesses data for analysis, and develops machine learning algorithms. They build predictive models for trend forecasting, aiding in business decisions. Additionally, they analyze data systems for efficiency, security, and optimization, playing a key role in transforming data into actionable insights.
Average Salary: The average annual salary for a data scientist in the United States is around $117,000, according to Glassdoor.
- Wrangling massive amounts of data
- Coding in programming languages such as Python and SQL
- Fluency with statistics, probability, calculus (both single variable and multivariable), and linear algebra
- Strong communication and the ability to use data to tell a story
A data engineer specializes in constructing robust data pipelines, facilitating the flow of data from various sources for real-time analytics. Their expertise lies in writing complex queries, making data readily accessible, and managing efficient data systems. They are also responsible for creating analytical tools for team use and formatting data to ensure it is usable for colleagues. Their role is pivotal in ensuring data is not just available but also functional and secure for organizational needs.
Average Salary: The average annual salary for a data engineer in the United States is around $113,000, according to Glassdoor.
- Programming languages such as Python, SQL, Java, and Scala
- A background in computer science, software engineering, or a related field
- Understanding data architectures and cloud computing
- Deep knowledge of automation and scripting
- Understanding ETL tools, data APIs, and distributed systems
A data architect is pivotal in shaping an organization’s data management framework. Their role involves thoroughly inventorying the organization’s data needs and seeking new data acquisition opportunities. They assess current data management practices and technologies to craft a comprehensive vision of data flow within the organization. Their responsibilities extend to designing, documenting, and deploying intricate database architectures, ensuring seamless data integration and centralization. Additionally, they are tasked with setting and maintaining database development standards, playing a key role in the overall data strategy and its effective implementation.
Average Salary: The average annual salary for a data architect in the United States is around $152,000, according to Glassdoor.
- Programming languages such as Python, Java, C++, and Perl
- Hadoop and NoSQL databases
- Predictive modeling, natural language processing, and text analysis
- Application server software such as Oracle, user interface and query software such as IBM DB2, and database management software such as Microsoft SQL Server
- Data modeling tools such as Visio, ERWin, and Enterprise Architect
A data modeler plays a crucial role in bridging the gap between business requirements and technical data solutions. Their primary responsibility is to transform complex business information into comprehensive data models, making it usable for computer systems. They work in close collaboration with data architects to develop and validate these models, ensuring they align perfectly with an organization’s needs. Additionally, data modelers are responsible for evaluating existing data systems, ensuring coding consistency within these systems, and updating and optimizing metadata models. This role is essential in ensuring that the data is not only accurately represented but also effectively utilized across various applications, maintaining compliance and efficiency.
Average Salary: The average annual salary for a data modeler in the United States is around $106,000, according to Glassdoor.
- Programming languages such as SQL
- Deep knowledge of metadata management
- Experience with physical and relational data modeling
- Interpersonal and communication skills
- Statistical analysis and mathematics
Big Data Engineer
Big data engineers are instrumental in managing and organizing vast amounts of user data, turning numerous clicks, taps, likes, swipes, shares, and purchases into analyzable information. Their key responsibilities include building efficient data pipelines and designing and managing robust data infrastructures, including big data frameworks and databases. They handle the complexities of large-scale data storage and are deeply involved in the ETL (Extract, Transform, Load) process. Through these activities, they ensure that the sheer volume of user data is not only manageable but also primed for meaningful analysis and insights. Their role is vital in harnessing the power of big data for organizational advantage.
Average Salary: The average annual salary for a big data engineer in the United States is around $125,000, according to Glassdoor.
- Programming languages such as Python, SQL, Java, and C++
- Automation and scripting
- ETL tools and data APIs
- Machine learning algorithms
- Data warehousing solutions
- Communication and interpersonal skills
Machine Learning Engineer
A machine learning (ML) engineer is a skilled programmer who develops self-operating software programs that leverage data to build predictive models. These engineers are pivotal in bridging the gap between data science and software development, crafting programs that enable machines to operate autonomously, without the need for direct human intervention.
The primary responsibilities of an ML engineer include designing sophisticated machine learning systems and researching as well as implementing advanced ML algorithms and tools. They play a crucial role in ensuring the quality of data used in these systems. Collaborating closely with managerial teams, they help define and achieve machine learning objectives tailored to specific organizational needs. Additionally, they tackle complex challenges involving multilayered data sets, applying their expertise to extract meaningful patterns and insights. This role is essential in the evolving landscape of technology, where machine learning is increasingly becoming a cornerstone of innovation and efficiency.
Average Salary: The average annual salary for a machine learning engineer in the United States is around $123,000, according to Glassdoor.
- Programming languages such as Python, Java, SQL, and R
- Computational linguistics, data analytics, artificial intelligence, and deep learning
- Machine learning frameworks, algorithm libraries, and software architecture
- Designing predictive models
- Mathematics, statistics, and algorithms
- Strong communication
AI engineers are at the forefront of developing systems and machines that mimic human cognitive functions, setting them apart in their ability to tackle complex problem-solving tasks. Their responsibilities include the development, testing, and deployment of AI models, which are central to creating intelligent machine behavior.
They are also responsible for building the data ingestion infrastructure, a critical foundation for processing and utilizing data effectively in AI systems. Evaluating and comparing the performance of various algorithms is another key aspect of their role, ensuring the most efficient and effective AI solutions are implemented.
Average Salary: The average annual salary for an AI engineer in the United States is around $118,000, according to Glassdoor.
- Programming languages such as Python, SQL, Java, C++, and R
- Statistics, applied mathematics, and algorithms
- Natural language processing
- Deep learning and neural networks
- Strong communication
- Creative problem-solving
An enterprise architect oversees and organizes an organization’s IT infrastructure, aligning it with business goals. They standardize IT systems, map IT assets and processes, update legacy systems, and advise on information integration strategies. Their role is crucial in ensuring the enterprise architecture supports and enhances the organization’s overall objectives.
Average Salary: The average annual salary for an enterprise architect in the United States is around $164,000, according to Glassdoor.
- Project management capabilities
- Understanding business models
- Strong knowledge of IT processes
- Strong leadership skills
- Clear written and verbal communication
- Analytical thinking and problem-solving
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Business Intelligence Analyst
A Business Intelligence (BI) Analyst plays a vital role in leveraging data to inform decision-making. They study industry trends and analyze company data to identify patterns that could impact an organization’s business strategy. By utilizing data effectively, they develop action plans aimed at fostering growth and refining the business strategy. Additionally, BI analysts are responsible for preparing detailed analytical reports, presenting their findings to stakeholders, thereby directly contributing to informed decision-making and strategic planning within the organization.
Average Salary: The average annual salary for a business intelligence analyst in the United States is around $87,000, according to Glassdoor.
- Data preparation
- Data mining
- Statistical analysis
- Descriptive analysis
- Data visualization
- Understanding of business models
Natural Language Processing (NLP) engineers specialize in creating systems that understand human language, combining expertise in computer science, information science, AI, and linguistics. Their key responsibilities include designing NLP systems and training and testing NLP models to ensure effective language processing. They also focus on defining the right datasets for language learning, crucial for training these models accurately. Finding and implementing suitable algorithms for NLP tasks is another critical part of their role, as is identifying text representations that can transform natural language into meaningful and useful features for various applications. This unique blend of skills positions NLP engineers at the forefront of advancing how machines interpret and interact with human language.
Average Salary: The average annual salary for an NLP engineer in the United States is around $88,000, according to Glassdoor.
- Knowledge of programming languages such as Python, Java, and R
- Strong understanding of data modeling, data structures, text representation, and semantic extraction techniques
- Strong understanding of statistics and classification algorithms
- Analytical and problem-solving skills
- Effective written and verbal communication
Database managers play a critical role in maintaining the performance, security, and integrity of an organization’s databases. Their responsibilities include optimizing database tools and services to ensure efficient operation. They are actively involved in monitoring database performance and implementing necessary improvements to maintain optimal functionality.
Conducting diagnostic tests and evaluating performance metrics are key aspects of their role, allowing them to identify and address any issues proactively. Additionally, they are responsible for testing databases for bugs and security flaws, ensuring the data’s safety and confidentiality. In situations of data loss, they are tasked with restoring the lost data, a crucial function for business continuity.
Average Salary: The average annual salary for a database manager in the United States is around $76,000, according to Glassdoor.
- Deep knowledge of database technologies
- Strong communication and interpersonal skills
- Leadership and management skills
- Strong organization and attention to detail
- Analytical problem-solving skills
Database developers, also known as database designers or programmers, are tasked with the crucial role of designing, building, and updating databases to meet an organization’s specific needs. Their responsibilities begin with the creation and development of databases, ensuring these systems are tailored to organizational standards and requirements.
A key part of their role involves evaluating existing databases to confirm they align with the organization’s standards. They also develop efficient storage and retrieval systems, which are fundamental for managing and accessing data effectively. Troubleshooting database issues and resolving malfunctions is another critical aspect of their job, ensuring the smooth operation of database systems.
Average Salary: The average annual salary for a database developer in the United States is around $97,000, according to Glassdoor.
- In-depth knowledge of current database processes
- Experience with BTL and business intelligence tools
- Knowledge of database objects such as indexes, views, statistics, and tables
- Programming languages such as SQL
- Data modeling
- Schema creation
- Familiarity with NoSQL database systems such as MongoDB and CouchDB
Data analysts mine primary and secondary sources to extract valuable information, playing a key role in informing business decisions. They clean data to eliminate errors and discrepancies, uncovering insights by identifying patterns, relationships, and trends. Additionally, they specialize in data visualization, making complex information easily accessible and understandable for stakeholders.
Average Salary: The average annual salary for a data analyst in the United States is around $74,000, according to Glassdoor.
- Programming languages such as SQL, Python, and R
- Familiarity with data analytics tools such as Tableau, Google Analytics, Jupyter, Amazon Web Services, and Microsoft Excel
- Data mining and cleaning
- Data warehousing
- Data visualization
- Strong communication
- Creative and analytical thinking
Compare All Jobs
|Python and SQL Coding, Statistical/Mathematical Fluency
|Python, SQL, Java, Scala; Data Architecture
|Python, Java, C++, Perl; Hadoop, NoSQL
|SQL Proficiency, Metadata Management, Data Modeling
|Big Data Engineer
|Python, SQL, Java, C++ Skills; Scripting
|Machine Learning Engineer
|Python, Java, SQL, R Expertise; Computational Linguistics
|Applied Math; NLP; Deep Learning, Neural Networks
|Project Management, Business Model Understanding, Leadership
|Business Intelligence Analyst
|Data Preparation, Data Mining, Data Visualization
|Python, Java, R; Data Modeling
|Interpersonal, Leadership Skills, Organizational Ability
|BTL, Business Intelligence Tool Experience, Understanding Database Objects
|SQL, Python, R
Highest Paying Data Science Companies
The more a company relies on data, the more it tends to pay its data scientists. Some of the highest-paying data science companies are social networking sites (who gather troves of user data), e-commerce platforms (who track billions of data points on consumer behavior), and real-time services (which depend on data to be responsive to customers).
Amazon’s data scientists work across all of its verticals, from parsing consumer purchase data on its e-commerce platform, to analyzing user behavior for its advertising division.
The average base salary for an Amazon data scientist is around $137,000, according to Glassdoor, although Amazon employees often make much more. In 2022, Amazon raised its base pay salary cap to $350,000 for corporate and tech employees, citing competition for tech talent.
Uber’s data scientists work in product analytics (e.g. ensuring the accuracy of the maps and geolocation technologies), finance and operations (e.g. using data to help the company with its financial planning and competitive strategy), and marketing (e.g. drawing on analytical insights to help improve growth, brand awareness, customer retention, and engagement).
The average base salary for an Uber data scientist is around $147,000, according to Glassdoor.
Meta’s data scientists work on products such as Facebook, Instagram, Oculus, Messenger, and WhatsApp. A data scientist focused on Instagram might help its monetization division drive a long-term strategy for revenue sharing. A business analyst at WhatsApp might collaborate with the sales and marketing teams to identify opportunities for user growth. And a machine learning engineer at Facebook might work on fraud detection technology.
The average base salary for a data scientist at Meta is around $152,000, according to Glassdoor. As one of the highest-paying technology companies in the world, it’s not unusual for data science professionals to make upwards of $200,000 at Meta.
Airbnb’s data scientists work on a range of products and features, from fraud prevention to helping set rental prices. As the company grows, so does its demand for data science professionals. Airbnb currently has dozens of open roles for data scientists, machine learning engineers, and analysts.
The average base salary for Airbnb data scientists is around $188,000, according to Glassdoor.
With more than 900 million active iPhones in the world, Apple is a data-collecting juggernaut. The company’s data science professionals work in all of its divisions, including hardware, software, music, and entertainment services.
The average base salary for Apple data scientists is around $158,000, according to Glassdoor.
Data scientists working for consulting firm Booz Allen are exposed to a range of clients and challenges, from helping commercial clients improve their cybersecurity, to assisting military clients.
The average base salary for Booz Allen data scientists is around $94,000, according to Glassdoor.
Microsoft’s size, reach, and product diversity mean that its data scientists get to solve a broad range of problems, from improving Microsoft Cloud to preventing bias in its language technologies.
The average base salary for Microsoft data scientists is around $139,000, according to Glassdoor.
How To Increase Your Current Data Science Salary
Expand Your Toolset
The number of tools that a data science professional has mastered can affect their compensation, according to O’Reilly’s Data Science Survey. Some salary-boosting toolsets include Apache Spark, Scala, and D3, which can add upwards of $15,000 to a compensation package. Familiarity with cloud computing will also boost salaries, with survey respondents who use Amazon Elastic MapReduce getting a salary boost of about $6,000.
O’Reilly also noted that those who used 15 or more tools could make up to $30,000 more than those who just used 10 to 14. There is a premium associated with mastering a variety of tools.
Where you choose to work can determine your salary. The O’Reilly Salary Survey found that the highest salaries went to data scientists in search/social networking companies, which makes sense given the amount of valuable data those companies can access (think LinkedIn, Meta, or Google).
It’s important to note that companies like Meta and LinkedIn also offer generous stock incentive bonuses, which easily add about $40,000 to $50,000 more when it comes to compensation.
Consider a New Location
Most people think of Silicon Valley as the region with the highest data science salaries. This is somewhat true. Payscale reports a 23% salary increase for workers in Mountain View, where Google and LinkedIn are headquartered. The O’Reilly report also infers that living in California is worth an extra $16,000 in salary.
But don’t forget to factor in the cost of living and state tax rates. For example, San Francisco is not where you can make the most as a data scientist. Neighboring San Jose has a lower cost of living and higher salary differential. In fact, when adjusting for cost of living and state taxes, Seattle trumps San Francisco. Likewise, cities such as Austin have lower base salaries for data scientists, but its low cost of living and no state taxes makes Austin comparable to other tech hubs.
Get a New Degree or Certification
In the O’Reilly survey, having a Master’s degree is correlated with a $1,000 a year salary increase. Adding a Ph.D. degree is correlated with a $9,000 yearly salary increase. However, a Ph.D. can quickly become a massive time investment (with an average of 8.2 years from start to dissertation), and if you spent that time gaining five years of experience in data science, you’d double the return on your annual salary.
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