9 Highest Paying Data Science Jobs in 2024

Data science professionals hold some of the most valuable skills in the job market in 2024. Despite recent layoff news, data science jobs are projected to grow by 35 percent from 2022 to 2032, according to the Bureau of Labor Statistics, which is much faster than the average for all occupations in the US. We’ve also seen that MAANG jobs are recovering.

Data science is a highly skilled and therefore lucrative domain. If you want to know more about the best paying jobs in data science, you’re in the right place. Read on to learn more about these roles and the skills you need to hone to secure such a position.

Although the average base pay for data scientists is around US$123,000, the highest base compensation at big tech firms could be anywhere between US$300,000 and $400,000 annually. Multiple factors will determine your potential salary, such as your experience, education, location, and industry.

Education or Skill

Whether highly specialized degrees, such as a Master’s in Data Science or a PhD in Statistics are required to secure top roles remains a contentious topic. There are certainly intangible value-adds to a technical education, such as an emphasis on foundational knowledge in data science, statistics, programming, and machine learning. Even today, a specialized degree will help you get your foot in the door much more easily.

The good news is that many top firms like Google and Meta are more interested in the skills (both technical and soft skills) you bring, and the impact you’ve created with your past work. Organizations value employees who are scrappy, keep up with advancements, and upskill well.

You can read more about the success stories of two Interview Query members — one with a Master’s in Business Analytics, and one who landed a job with no data science education or relevant experience.

Seniority

Years of experience are by far the biggest factor that influences salaries. We’ve analyzed the data, and there is, on average, a 2-2.5x difference between entry-level and senior-level positions’ compensation. So once you have a few years of experience under your belt, the base salary and total compensation numbers skyrocket.

Location

Areas with a high cost of living and tech ecosystems, like San Francisco and Seattle typically offer higher salaries to attract and retain top talent. However, with the advent of remote work, it’s also important to look at normalized salaries to account for the cost of living. With this additional data point, we’ve concluded that Austin, Houston, Cincinnati, and Boise are the best cities to live in considering both compensation and cost of living.

Industry

The industry in which you’ll work will significantly impact your salary. This is because of various factors, such as:

• Demand: Some industries have a higher demand for data-driven decisions than others, like technology, finance, and healthcare.
• Resources: Tech giants like Google, Amazon, and Meta can afford top-tier salaries to attract top talent. Conversely, non-profit organizations or educational institutions may value data science but are limited by smaller budgets.
• Criticality: In finance, for instance, data scientists who develop algorithms for automated trading or risk management are critical to the bottom line. Similarly, in e-commerce, improving personalization algorithms directly influences sales, hence those roles are better compensated.
• Regulatory impact: Data scientists in pharma companies require specialized training that requires adherence to strict regulatory standards, thus commanding higher salaries.
• R&D focus: Industries that focus on innovation pay more, as these roles drive the creation of new products. For instance, data scientists in an R&D-intensive AI startup would earn more than those in more traditional sectors.

Finally, economic trends play a significant role. Now that data privacy and cybersecurity are major concerns, industries that rely on data scientists for threat analysis will have higher salaries because of the increased importance of these roles.

Understanding these factors will help you target the industry that aligns with your passion, skillsets, and financial goals.

1. Data Scientist - $318,757

Put simply, a data scientist analyzes large amounts of data to generate actionable insights for business stakeholders. The role involves a blend of statistical analysis, machine learning, data interpretation, and reporting. You need to be a “data detective” — someone who can leverage data to find significant patterns and anomalies.

Salary: The average base salary is US$123,080 in the United States, with the highest base compensation offered by companies like Netflix, at around US$318,757.

Skills required:

• Statistical analysis and mathematical skills: You’ll need a solid foundation in statistics, probability, linear algebra, and related mathematics concepts.
• Programming: Proficiency in programming languages such as Python, R, and SQL.
• Machine learning: Foundational knowledge of machine learning algorithms, and the ability to select the right model in the correct business context.
• Data visualization: Skills in data visualization tools like Tableau, and PowerBI, or libraries such as Matplotlib and Seaborn in Python.
• Communication: Excellent communication skills are essential to translate technical findings to non-technical teams and external stakeholders.
• Problem-solving: You should be a good critical thinker with strong business acumen.

Here is our detailed guide to landing a data scientist role in 2024.

2. Machine Learning Engineer - $455,167

Machine learning engineers design and implement machine learning systems. As an MLE, you are expected to create algorithms, automate and fine-tune predictive models, and deploy solutions at scale. This work is necessary to develop AI-driven products such as recommendation engines and automated trading systems.

Salary: The average base salary is US$148,720 in the United States, with the highest base compensation offered by companies like Netflix, at around US$455,167.

Skills required:

• Programming: Proficiency in programming languages like Python, Java, or C++.
• ML algorithms: This role requires deep knowledge of machine learning algorithms and their applications. In addition to theoretical concepts, you’ll need to know how to apply concepts in real-world optimization scenarios.
• Statistics and probability: A strong foundation in statistics and probability is needed to make accurate predictions about model performance.
• Systems design: Typically for more senior roles, you’ll be expected to know how to create a high-level design for a search algorithm or recommender system.
• Deep learning: Knowledge of deep learning frameworks such as TensorFlow, Keras, or PyTorch is often advertised in job roles.

Resource: We have compiled a list of our top picks for machine learning projects, and machine learning algorithm interview questions for those of you looking to interview.

3. AI Architect - $326,000

As artificial intelligence gains traction worldwide, organizations need to build adequate architecture rapidly. That’s why AI architects will be in demand in 2024 and beyond — they will play a key role in facilitating widespread AI adoption.

An AI architect designs and oversees the infrastructure that supports artificial intelligence systems within an organization, to ensure that AI solutions are scalable, sustainable, and integrated with existing systems. AI architects work closely with data scientists, machine learning engineers, and IT teams to create comprehensive strategies.

Salary: The average salary typically falls between US$122,000 to US$171,000 annually, with firms like Intel offering up to US$326,000 in base pay.

Skills required:

• Advanced programming: You’ll need to be experienced in multiple programming and markup languages such as Python, Java, Ruby, JSON, etc.
• Systems design: Strong ability in system architecture and understanding of how to build scalable and efficient AI systems is coveted.
• Big data technologies: Knowledge of big data technologies like Hadoop, Spark, and Kafka is also often advertised.
• Problem solving: Considering how new this role is in certain industries, you’ll need to be an independent thinker who can identify business needs and creatively solve problems.

Apart from the above, performance modeling, computer architecture, and hands-on industry experience are generally asked for by employers.

4. Quantitative Analyst - $292,045

Quantitative analysts apply mathematical methods to financial and risk management problems. Investment banks, hedge funds, and trading firms pay big bucks to quantitative analysts. Quants typically develop predictive models to analyze trends in order to inform investment strategies.

Salary: The average base pay is around US$149,550, while the highest base pay can be approximately US$292,045 annually. Experienced quants in areas like algorithmic trading can earn even more with bonuses and profit-sharing.

Skills required:

• Advanced mathematics: Companies want a strong background in mathematics and statistics as these skills are required to create complex models and understand financial problems.
• Programming: Proficiency in programming languages such as Python, R, C++, or Java is also a valued skill.
• Financial knowledge: You’ll need to be well-versed in concepts like derivatives pricing, risk management, and portfolio theory.
• Communication: Quants must also be able to explain their complex models to non-quantitative colleagues, like traders and regulatory compliance officers.

Resource: If you’re planning to apply to a quant role, you can explore our article on general quant interview questions, or statistics and probability interview questions for quants. We’ve also written a guide to data science roles at hedge funds.

5. Data Engineer - $286,730

As a data engineer, your primary responsibility will be to design, test, and maintain scalable data management systems. Data engineers also build algorithms to help data science teams access data and work to improve data reliability and quality.

Salary: The average base salary is US$107,307 in the United States, with the highest base compensation offered by companies like Netflix, at around US$286,730.

Skills required:

• Programming: You’ll need to know Python, Java, and Scala that are commonly used langauges for scripting and data handling.
• Database management: Data engineers usually need to have experience in database technologies such as MySQL, PostgreSQL, and newer NoSQL databases like Cassandra or MongoDB.
• Big data tools: Familiarity with tools such as Apache Hadoop, Spark, and Kafka for working with big data is required knowledge.
• Data modeling: Knowledge of entity-relationship modeling, normalization and denormalization tradeoffs, dimensional modeling, and related concepts are required.
• ETL Tools: Experience with ETL (extract, transform, load) tools and methodologies to populate data warehouses is usually asked for by employers.

Resource: We’ve written a career guide on becoming a data engineer in 2024. You can also look into our list of top data engineering interview questions for practice.

6. Machine Learning Scientist/Research Scientist - $244,500

Unlike a data scientist, a machine learning scientist has a research and development role. ML scientists or ML research scientists develop new algorithms and techniques in machine learning and artificial intelligence. This role demands experimental research and often involves significant theoretical work, along with testing in areas such as deep learning, neural networks, and predictive analytics.

Research roles typically require a PhD in data science, statistics, or a related field, a background in robotics, AI, or computer vision, or experience with experimental design. Almost all MAANG companies hire ML scientists exclusively from various PhD programs.

Salary: The average machine learning scientist salary in the US is US$161,505 annually, with some firms offering up to US$244,500 yearly.

Skills required: Machine learning engineers and scientists require the same technical skills: Python, SQL, algorithms, etc.

The key difference is that machine learning scientists need to have a strong background in research. They must know how to conduct experimental and quasi-experimental trials and be skilled at documenting and presenting research.

Another difference is that machine learning researchers often have more specialized ML knowledge within a particular domain, like probabilistic models or the Gaussian process.

7. Enterprise Architect - $201,182

An enterprise architect oversees the IT infrastructure of their company, ensuring that it aligns with the organization’s business goals. They are responsible for overseeing, improving, and upgrading enterprise services, both software and hardware. Companies are willing to pay for experienced EAs who are skilled at planning for and predicting future demand.

Salary: The average salary is typically US$143,219 per year, with the highest pay being approximately US$201,182 annually.

Skills required:

• Broad technical acumen: A highly paid EA is knowledgeable about a range of IT infrastructure solutions, including hardware, software, networks, and cloud services.
• Strategic planning: They should be able to develop long-term IT strategies to align with the organization’s business objectives.
• Systems integration: They’ll also need to integrate a variety of applications into a cohesive environment to support operational needs.
• Project management: These roles generally require more experienced employees, that is people who can manage large-scale IT projects, including planning, budgeting, and execution.

8. NLP Engineer - $230,000

NLP engineers specialize in developing NLP algorithms for applications including text classification, sentiment analysis, named entity recognition, and machine translation. They are also tasked with model training, evaluation, and deployment, optimizing performance and collaborating with cross-functional teams to deliver on business objectives.

Salary: The average salary for an NLP Engineer in the United States is around US$170,000, with the highest pay reaching US$230,000 annually.

Skills required:

• Programming: You’ll need to be proficient in Python or Java, as these languages are commonly used in NLP projects.
• Machine learning and deep learning: You will be expected to have a thorough grasp of machine learning algorithms and deep learning frameworks like TensorFlow or PyTorch, especially in the context of NLP.
• NLP and linguistics: Although it is not essential, having a primitive understanding of linguistics may be helpful for NLP engineers. Language comprehension and rule-based NLP techniques including tokenization, parsing, semantic analysis, and word embeddings are good to know.

Resource: Here are some NLP projects and datasets you could explore if this is a role you’re interested in.

9. Database Manager - $152,708

A database manager is expected to oversee the operation of an organization’s databases and ensure top-notch performance and security. In this role, you would manage a team of database professionals, develop database strategy, and evangelize best practices for database development. Database managers also coordinate with IT and data teams to support business applications and user requirements.

Salary: The average salary is about US$92,560 per year, with higher bands reaching about US$152,708 annually.

Skills required:

• Database technologies: You’ll need to have strong knowledge of database management systems such as Oracle, SQL Server, MySQL, and newer NoSQL technologies like MongoDB or Cassandra.
• Data modeling: Strong skills in data architecture and modeling techniques are needed.
• Security management and project management: Employers will expect you to be well-versed in database security management practices and have experience managing projects.
• Leadership: Strong leadership skills to manage and mentor a team, along with clear communication skills to liaise with different departments and report to upper management are needed to secure this job.

What skills do I need for a data science position?

Depending on the role, you’ll need a mix of technical and soft skills. Key technical skills include proficiency in SQL, Python, and cloud platforms, along with a good grasp of statistics, domain knowledge, and machine learning.

You can explore your desired role through one of our tailored learning paths.

Don’t underestimate the role of soft skills in landing a lucrative position, either. Employers look for people who can take initiative, display critical thinking, and communicate well within and outside their teams.

Are there job postings for data science roles on Interview Query?

You can visit our job portal. There, you can sort the list by team, location preference, and your current skillsets and apply for your desired role.

How do I make my resume better?

Tailor it to applications by highlighting relevant skills and experiences. Use quantifiable achievements to demonstrate your capabilities and if you have limited work experience, include relevant projects you’ve worked on, on your own. Finally, ensure your resume is clear, concise, and error-free, and include any relevant coursework or certifications.

Here at Interview Query, we have multiple learning paths, interview questions, and both paid and free resources you can use to upskill for your dream role. You can access specific interview questions, participate in mock interviews, and receive expert coaching.

If you have a specific company in mind to apply to, check out our company interview guide section, where we have detailed company and role-specific preparation guides.

We’d like to wrap up by saying that landing the right job with a good pay package is achievable, once you do your research and make a strategy for your application process. You should also consider demonstrating your skills in practical settings, such as through personal projects or contributions to open-source platforms.

We hope this discussion has been helpful. If you have any other questions, don’t hesitate to reach out to us or explore our blog.