15 Highest Paying Data Science Jobs in 2024: Data Science Salary Insights

The data science field has evolved, leading to the emergence of specialized roles like machine learning engineers, data engineers, and data analysts. Each of these roles requires unique skill sets and responsibilities, making understanding what impacts data science salary trends in today’s job market essential.

Despite the big paychecks being more concentrated in high-cost areas, data science is among the highest-paid occupations in the US, with a median annual wage of $108,020 in 2024. While your technical expertise as a data professional significantly contributes to a higher salary, networking and relationships also play major parts in securing the highest-paying data science jobs in 2024.

You’ve come to the right place to understand the obligations involved in data science job roles and their associated salaries. Read on to discover high-paying positions and the skills that make them in demand.

A data scientist is analytical and strives to develop predictive models, test hypotheses, and analyze data to predict market trends, risks, and threats. They are responsible for harvesting, analyzing, and interpreting huge amounts of data to offer recommendations and aid business decisions.

Data scientists are expected to collaborate with diverse stakeholders, including engineers, business managers, and other data specialists, to understand business requirements and provide insights that help the company achieve its financial and product goals. Excelling in these roles, data scientists come from a variety of academic backgrounds and experiences, such as:

1. Statisticians

Statisticians provide a strong foundation in statistical modeling, hypothesis testing, and data analysis. Their expertise is crucial for understanding data distributions, identifying patterns, and making inferences.

2. Computer Scientists

Computer scientists bring a deep understanding of algorithms, data structures, and programming languages. They are essential for developing efficient data pipelines, machine learning models, and software tools.

3. Mathematicians

Mathematicians provide a solid foundation in mathematical concepts like linear algebra, calculus, and optimization. Their skills are valuable for developing complex algorithms and understanding the theoretical underpinnings of machine learning techniques.

4. Domain Experts

Domain experts offer specialized knowledge of a particular industry or domain. Their insights help tailor data science solutions to specific business needs and ensure that models are interpretable and relevant.

5. Economists

Economists bring expertise in economic theory and analysis. Their understanding of financial relationships and causal inference can be valuable for analyzing economic data and making policy recommendations.

6. Business Analysts

BAs understand business processes, requirements, and objectives. They can help identify data-driven opportunities, communicate findings to stakeholders, and ensure data science projects align with business goals.

7. Social Scientists

Social scientists focus on human behavior, social interactions, and cultural factors. Their insights advance understanding of the societal implications of data science applications and ensure ethical and responsible use of data.

Here are the 15 highest-paying data science jobs in 2024 that you must explore if you have a passion for data and love working in fast-paced environments:

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. You might also be required to have experience scaling and optimizing production models, working closely with data scientists and software engineers.

Salary: The average base salary for a machine learning engineer is $148,326 in the United States, while the highest base compensation offered by companies like Netflix is around $455,167.

Skills required:

• Programming: Proficiency in programming languages like Python, Java, or C++.
• ML algorithms: 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 to make accurate predictions about model performance.
• Systems design: Typically, for more senior roles, the ability 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.

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.

Senior 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” who can leverage data to find significant patterns and anomalies.

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

Skills required:

• Statistical analysis and mathematical skills: 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.
• Advanced Skills: Experience with causal inference, A/B testing, or deploying machine learning models.
• Data visualization: Skills in data visualization tools like Tableau, PowerBI, or libraries such as Matplotlib and Seaborn in Python.
• Communication: Excellent communication to translate technical findings to non-technical teams and external stakeholders.
• Problem-solving: Critical thinking with strong business acumen. For senior roles, mentorship and leadership skills in data-driven decision-making could also be necessary to succeed as a senior data scientist.

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

AI Architect - $326,000

As artificial intelligence gains traction worldwide, organizations must rapidly build adequate architecture. 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 $122,000 to $171,000, with firms like Intel offering up to $326,000 in base pay.

Skills required:

• Advanced programming: Experience with multiple programming and markup languages such as Python, Java, Ruby, JSON, etc.
• Systems design: Strong ability in system architecture and an understanding of how to build scalable and efficient AI systems.
• Big data technologies: Knowledge of big data technologies like Hadoop, Spark, and Kafka.
• Problem-solving: An independent thinker who can identify business needs and creatively solve problems, considering how new this role is in certain industries.

Apart from the above, employers generally ask for performance modeling, computer architecture, and hands-on industry experience. Experience in AI ethics, ensuring explainability, and facilitating integration with enterprise data ecosystems could also be relevant.

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. They also work in healthcare, energy sectors, and tech. Quants typically develop predictive models to analyze trends to inform investment strategies.

However, many quant roles also require proficiency in coding languages such as C++ for high-performance computing, developing backtesting frameworks for model validation, and engaging in derivative pricing activities.

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

Skills required:

• Advanced mathematics: 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.
• Financial knowledge: Knowledge of derivatives pricing, risk management, and portfolio theory.
• Communication: The ability to explain complex models to non-quantitative colleagues, like traders and regulatory compliance officers.

Resource: If you plan 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 for data science roles at hedge funds.

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 $107,307 in the United States, with the highest base compensation offered by companies like Netflix, at around $286,730.

Skills required:

• Programming: Knowledge of Python, Java, and Scala, commonly used languages for scripting and data handling.
• Database management: 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.
• Data modeling: Knowledge of entity-relationship modeling, normalization and denormalization tradeoffs, dimensional modeling, and related concepts.
• ETL Tools: Experience with ETL (extract, transform, load) tools and methodologies to populate data warehouses.

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

Machine Learning 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 and 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.

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

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

The key difference is that machine learning scientists must have a strong background in research. They need to 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.

NLP Engineer - $230,000

NLP engineers specialize in developing NLP algorithms for applications, which include 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 $170,000, with the highest pay reaching $230,000.

Skills required:

• Programming: Proficiency in Python or Java, as these languages are commonly used in NLP projects.
• Machine learning and deep learning: 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 not essential, a fundamental understanding of linguistics. 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.

Data Architect - $188,742

A data architect is responsible for designing and managing an organization’s data infrastructure. This role involves creating blueprints for data management systems to ensure they align with business objectives and are scalable, secure, and efficient. Data architects work closely with data engineers and analysts to build robust systems for storing, processing, and analyzing data.

Salary: The average base salary for a data architect in the U.S. is around $144,000, with top-tier companies like Amazon and Google offering up to $279,000.

Skills required:

• Database design and management: Proficiency in database technologies such as SQL, NoSQL (e.g., MongoDB), and data modeling concepts like normalization.
• Data warehousing: Knowledge of data warehousing solutions like Amazon Redshift, Snowflake, or Google BigQuery.
• Cloud computing: Experience with cloud platforms such as AWS, Azure, or Google Cloud for managing and scaling data infrastructure.
• ETL processes: An understanding of ETL (extract, transform, load) pipelines and tools for integrating data from various sources.
• Big data technologies: Familiarity with big data frameworks like Hadoop, Spark, and Kafka.
• Data governance and compliance: For senior roles, experience in data governance, compliance with data privacy regulations (GDPR), and optimizing data infrastructure for performance.
• Problem-solving and strategic thinking: Ability to design systems that balance performance, cost, and scalability while aligning with business needs.

Resource: You can explore our data architect interview questions on landing this role.

Enterprise Architect - $201,182

An enterprise architect oversees the IT infrastructure of their company, ensuring 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 skilled at planning for and predicting future demand.

Salary: The average salary is typically $143,219, with the highest at around $201,182.

Skills required:

• Broad technical acumen: Knowledge of IT infrastructure solutions, including hardware, software, networks, and cloud services.
• Strategic planning: Ability to develop long-term IT strategies to align with the organization’s business objectives.
• Systems integration: Skills in integrating a variety of applications into a cohesive environment to support operational needs.
• Project management: In general, experienced people who can manage large-scale IT projects, including planning, budgeting, and execution.

Apart from these, you may focus on learning compliance with regulations, risks, security concerns, and the effectiveness of enterprise architecture to strengthen your candidacy.

Application Architect - $148,738

An application architect designs the architecture and framework for software applications within an organization. Application architects ensure that all applications are scalable, reliable, and integrated with the company’s broader IT infrastructure. They also work closely with development teams and other IT professionals to create solutions that meet business requirements while maintaining security and efficiency.

Salary: The average base salary for an application architect in the U.S. is around $148,738, with top companies offering up to $192,650.

Skills required:

• Software architecture: Deep knowledge of designing scalable software systems using design patterns, microservices, and architecture frameworks.
• Programming: Proficiency in programming languages like Java, C#, Python, or JavaScript for building and integrating applications.
• Cloud platforms: Experience with cloud-based solutions (e.g., AWS, Azure, Google Cloud) for deploying and managing applications.
• Systems integration: An understanding of how to integrate various software applications and services within a company’s IT ecosystem.
• DevOps practices: Knowledge of continuous integration/continuous deployment (CI/CD) pipelines and automation tools for software deployment. Consider gathering experience in conducting architectural reviews, addressing technical debt, and utilizing tools like UML for creating architectural diagrams.
• Communication and leadership: Strong ability to work with cross-functional teams, guiding development efforts and ensuring alignment with business goals.

Business Intelligence Developer - $122,926

A business intelligence (BI) developer transforms raw data into meaningful insights that drive business decisions. Their role revolves around building, maintaining, and optimizing BI systems and dashboards. They work with large datasets, extract key metrics, and present them in visually compelling ways through tools like Power BI or Tableau. BI developers collaborate with stakeholders across departments to ensure data solutions align with business goals.

Salary: The average base salary for a BI developer in the U.S. is around $122,926, with experienced professionals at top companies earning up to $145,000.

Skills required:

• Data modeling and analytics: Strong understanding of data modeling, relational databases (SQL Server, MySQL), and data warehousing concepts.
• BI tools: Proficiency in business intelligence tools like Power BI, Tableau, or Looker for creating data visualizations and reports. Emphasize knowledge in optimizing dashboard performance and leveraging scripting for data manipulation, such as using DAX in Power BI or MDX in OLAP cubes.
• SQL and programming: Expertise in SQL for querying databases and, in some cases, experience with Python or R for more advanced analytics.
• ETL processes: Experience with ETL tools and processes for cleaning, transforming, and loading data into data warehouses.
• Communication: Ability to communicate technical insights to non-technical stakeholders and work closely with teams to translate business needs into data-driven solutions.

Resource: Check out our BI developer interview questions to strengthen your skills for this role.

Business Intelligence Analyst - $87,672

Business intelligence analysts are responsible for transforming data into actionable insights that guide strategic business decisions. They analyze data trends, monitor key performance indicators (KPIs), and provide reports or dashboards to executives and department heads. BI analysts work closely with both data teams and business stakeholders to ensure that data-driven strategies align with organizational goals.

Salary: The average base salary for a BI analyst in the U.S. is around $87,000, with senior professionals at larger firms earning up to $175,000.

Skills required:

• Data analysis: Strong analytical skills to interpret complex datasets and identify trends, opportunities, and challenges.
• BI tools: Proficiency in tools like Tableau, Power BI, or Qlik to create dashboards and data visualizations.
• SQL: Expertise in SQL for querying databases and retrieving data for analysis.
• Data storytelling: Ability to present insights in a compelling and easy-to-understand way for non-technical stakeholders. Additional skills beneficial for the role include translating business questions into data requirements, managing data quality issues, and demonstrating proficiency in scripting languages for data preparation.
• Problem-solving: Strong problem-solving skills to address business challenges using data-driven approaches.

Resource: If you’re preparing for a BI analyst interview, check out our BI analyst case studies for practice questions and tips.

Data Analyst - $82,669

Data analysts play a vital role in helping organizations make informed decisions by examining and interpreting complex datasets. They collect, process, and perform statistical analyses on data to uncover trends and insights that can influence business strategies. Data analysts collaborate with various teams to understand their data needs and deliver meaningful reports and visualizations.

Salary: The average base salary for data analysts in the U.S. is approximately $82,000, with more experienced analysts earning up to $145,000 at top companies.

Skills required:

• Statistical analysis: Proficiency in statistical techniques and methods to interpret and analyze data effectively.
• Data visualization: Expertise in tools like Tableau, Power BI, or Google Data Studio to create visual representations of data findings.
• SQL proficiency: Strong skills in SQL for querying databases and managing data extraction processes.
• Excel skills: Advanced Excel capabilities for data manipulation, analysis, and reporting.
• Critical thinking: Ability to approach data challenges creatively and derive actionable insights for business stakeholders.

Resource: To boost your interview preparation, check out our data analyst interview questions for helpful practice questions and strategies. For further skill development, work through our SQL data analyst interview questions.

Statistician - $84,792

Statisticians use mathematical theories and models to analyze and interpret data, helping organizations make data-driven decisions. Their work spans various industries, from healthcare and finance to government and sports analytics. Statisticians identify trends, run experiments, and apply statistical methods to solve real-world problems. The role requires a strong foundation in mathematics, statistical software, and data interpretation.

Salary: The average base salary for statisticians in the U.S. is around $84,792, with top professionals in fields like finance or pharmaceuticals earning up to $142,000 annually.

Skills required:

• Mathematics and statistics: In-depth knowledge of probability theory, regression analysis, hypothesis testing, and advanced statistical techniques. Also, focus on skills in predictive modeling, time series analysis, and survey data analysis, as these are key areas where statisticians typically contribute.
• Statistical software: Proficiency in tools like R, SAS, SPSS, or STATA for conducting analyses and building models.
• Data interpretation: Ability to draw meaningful insights from data and effectively communicate findings to decision-makers.
• Experimental design: Expertise in designing experiments and surveys, as well as analyzing experimental data for accuracy and reliability.
• Programming: Familiarity with programming languages like Python or R for data manipulation and automation.

Resource: For those interested in pursuing a statistician role, check out our statistics and probability interview questions to get started.

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 strategies, and promote 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 $92,560, with some reaching about $152,708.

Skills required:

• Database technologies: A 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.
• Security and project management: Experience in database security management practices and 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.

Resources: Feel free to utilize our AI Interviewer and Mock Interview Panel to refine your responses for the database manager jobs in our job board.

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.

Here are some factors that may dictate the financial package you get offered in your new data science role in 2024:

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 the biggest factor influencing data science 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 data science 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. As per US Labor Statistics data), San Jose, San Francisco, Seattle, and New York are the best states to live in, considering both compensation and cost of living.

Industry

Your chosen industry will significantly impact your data science salary due to various factors, such as:

• Demand: Some industries, like technology, finance, and healthcare, have a higher demand for data-driven decisions than others. Furthermore, 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 demand for these roles.
• 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.

Recession and Other Factors

Finally, economic trends play a significant role. If customers are spending less, don’t expect your employer to significantly increase your salary in upcoming months. During periods of economic uncertainty, companies may prioritize cost-cutting measures, which could include hiring freezes or lower compensation packages.

However, data science remains a critical function for many businesses, as data-driven decisions can enhance efficiency and mitigate risk, even in tough times. Understanding these factors will help you target the industry that aligns with your passion, skillsets, and financial goals.

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 the 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 through on your own. Finally, ensure your resume is clear, concise, error-free, and include relevant coursework or certifications.

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.

At Interview Query, we offer multiple learning paths, interview questions, and both paid and free resources to help you upskill for your dream role. Access specific interview questions, participate in mock interviews, and receive expert coaching.

If you’re targeting a specific business, visit our company interview guide section for tailored preparation guides.

Landing a well-paying job is achievable with the right research and a solid application strategy. Consider showcasing your skills through personal projects or contributions to open-source platforms.

We hope this information is helpful. For any questions, feel free to reach out to us or explore our blog.

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.