10 Data Science Jobs That Are in Demand

Data science continues to be a vital field, sparking innovation in everything from healthcare to finance. Even with the tech layoffs of 2023, data science jobs were largely spared, highlighting their importance to business growth.

One role that's really taking off is the AI data scientist, where people blend regular data science skills with specialized AI knowledge. More companies are using AI tools, so they need data scientists who actually understand how to work with artificial intelligence technologies.

Impact of AI and Automation on Job Demand

The World Economic Forum's Future of Jobs Report 2025 finds that emerging technologies will create many more jobs than they cut. About 170 million new jobs are projected globally by 2030 due to tech adoption, even as ~92 million roles are displaced―a net +78 million increase in employment.

In the same report, the WEF forecasts that AI and data processing trends will create 11 million new jobs by 2030 and replace about 9 million, still yielding a net gain of jobs overall. In other words, the AI revolution is set to add jobs on balance, even as certain tasks become automated.

Why Choose a Career in Data Science?

Pursuing a data science career offers major advantages, including strong job security, room for advancement, and work that feels meaningful.

If you're thinking about a career in data science, here are three solid reasons it's a smart move:

• Job Security: According to the U.S. Bureau of Labor Statistics, data scientist employment is projected to grow by 36% from 2023 to 2033, significantly outpacing the average growth for all occupations.
• Growth Opportunities: Data science is constantly evolving, providing ongoing opportunities to learn, grow your skills, and advance your career as technology progresses.
• Meaningful Work: As a data scientist, you get to solve puzzles that have real consequences, turning messy data into insights that actually change how teams work.

All of this means data science is a smart career bet. While other tech jobs come and go, the need for people who can work with data just keeps growing.

So What Exactly Does a Data Scientist Do?

Basically, they tackle more advanced analysis than data analysts, build predictive models and apply sophisticated techniques to improve outcomes. This article will walk you through some of the most promising data science jobs, detailing specific responsibilities, salary ranges, and must-have skills for each role. By the end, you'll:

• Discover 10 in-demand data science jobs
• Understand key responsibilities, salary ranges, and skills for each position
• Get tips for gaining practical experience to advance your career

Whether you're looking to become a data analyst or level up to a data scientist position, understanding the nuances of these different data roles will help you chart the right course. We'll cover the key details of each one, and how to gain the practical skills that can take your career to new heights.

Let's get started!

Top 10 Data Science Jobs

Here are the top 10 data science jobs that are currently shaping the industry:

1. Data Engineer
2. Database Administrator
3. Data Architect
4. Data Scientists:
   1. Traditional Data Scientist
   2. AI Data Scientist
5. Machine Learning Engineer
6. Deep Learning Engineer
7. Business Intelligence Developer
8. Data Translator
9. Data Privacy Officer
10. AI Engineer

1. Data Engineer

Data engineers are the backbone for building, maintaining, and optimizing the technical infrastructure that powers data-driven decision-making. While data scientists focus on analyzing data to uncover insights, data engineers work behind the scenes, ensuring data is reliably collected, stored, and accessible to analysts and scientists.

On a typical day, a data engineer might design databases, pull data from APIs, write scripts to transform datasets, build automated pipelines, and manage cloud infrastructure. They collaborate closely with data analysts and scientists to ensure datasets are ready for analysis and reporting.

Salary: \$102K–\$168K/yr (Glassdoor)

Responsibilities

• Build scalable data pipelines to transform and process large datasets efficiently.
• Maintain robust databases and data warehouses for easy access and analysis.
• Ensure efficient data collection from APIs and external data sources.
• Develop and automate data workflows using tools like Apache Airflow and dbt.
• Collaborate with stakeholders to understand data needs and provide technical solutions.

Key Skills

• Coding expertise in languages such as Python, Java, or Scala.
• Deep knowledge of SQL and NoSQL databases.
• Hands-on experience with big data frameworks like Hadoop and Apache Spark.
• Cloud infrastructure experience on platforms such as AWS, Azure, or Google Cloud.
• Ability to implement CI/CD pipelines and automated data processes.

Companies need skilled data engineers to handle all the technical work that makes analytics and machine learning actually possible. Without them, data teams would spend most of their time fixing broken pipelines instead of finding insights.

2. Database Administrator

Database Administrators (DBAs) safeguard an organization's data by managing, securing, and optimizing database infrastructure. While data scientists analyze and interpret data for strategic insights, DBAs ensure databases operate smoothly, securely, and efficiently, providing a robust foundation for all data-driven activities.

On a typical day, a DBA might monitor database performance, manage user access and security, and execute backup and recovery plans. They also collaborate closely with IT teams to implement new database systems, fine-tune database queries, and proactively troubleshoot issues to maintain optimal performance.

Salary: \$83K–\$137K/yr (Glassdoor)

Responsibilities

• Design, implement, and manage database systems and infrastructures
• Ensure efficient data storage, retrieval, and performance optimization through regular tuning
• Develop and oversee database backup, recovery, and security protocols
• Monitor system performance, proactively identifying and resolving database issues
• Coordinate with technical teams to integrate databases with existing software and infrastructure
• Ensure compliance with data privacy regulations (GDPR, CCPA) and internal governance policies

Key Skills

• Proficiency in SQL and relational databases (Oracle, MySQL, PostgreSQL)
• Familiarity with NoSQL databases (MongoDB, Cassandra) and cloud-based database solutions (AWS, Azure)
• Experience with database optimization techniques and query performance tuning
• Strong analytical thinking and problem-solving skills for identifying and resolving technical issues quickly
• Excellent collaboration and communication skills to work effectively with technical and non-technical stakeholders

Every company needs Database Administrators because when databases break, everything stops. They handle all the behind-the-scenes work that keeps databases secure and running well, so other teams can focus on using the data instead of fixing technical problems.

3. Data Architect

Data architects design and maintain comprehensive frameworks for managing an organization's data effectively. While data scientists analyze data to generate actionable insights, data architects define the overarching strategies and structures to manage data at scale, ensuring long-term usability, quality, and compliance throughout the company.

On a typical day, data architects might develop strategic data management policies, design complex data models, and oversee data governance initiatives. They collaborate with senior stakeholders, IT teams, and data professionals to ensure that data infrastructure aligns with long-term business goals and regulatory requirements.

Salary: \$134K–\$221K/yr (Glassdoor)

Responsibilities:

• Develop and implement robust data strategies that support business objectives.
• Create and manage detailed data models to ensure efficient data storage and accessibility.
• Oversee data governance, compliance, and security measures.
• Coordinate with IT teams to select and integrate appropriate data management technologies, such as cloud platforms.
• Maintain data quality and integrity across diverse databases and systems.

Key Skills:

• Expertise in data modeling methodologies, including relational and dimensional modeling.
• Strong proficiency with big data technologies (e.g., Hadoop, Apache Spark) and cloud data services (AWS, Azure, Google Cloud).
• Knowledge of data warehousing solutions and modern data architecture best practices.
• Analytical abilities to align complex technical solutions with business needs.
• Deep understanding of data compliance and privacy regulations (GDPR, CCPA).

Data architects make sure a company's data is organized in a way that's actually useful and easy to work with. As data continues to grow in volume and complexity, their role becomes increasingly important in shaping a company's ability to use data effectively and ethically.

4a. Traditional Data Scientist

Data scientists play a pivotal role in transforming complex data into actionable insights that drive business strategy. Their expertise in statistical analysis and machine learning enables organizations to make informed decisions and stay competitive in a data-driven world.

On a typical day, a data scientist might collect and clean datasets, develop and test predictive models, and present findings to stakeholders. They collaborate with cross-functional teams to ensure that the insights derived from data are effectively integrated into business processes, enhancing efficiency and innovation.

Salary: \$119K–\$193K/yr (Glassdoor)

Responsibilities

• Analyze large sets of structured and unstructured data to identify trends and patterns
• Develop predictive models and machine learning algorithms to solve complex business problems
• Collaborate with engineering and product teams to implement data-driven solutions
• Communicate findings and insights to stakeholders through reports and visualizations

Key Skills

• Proficiency in Python, R, and SQL for data analysis and manipulation
• Strong understanding of machine learning algorithms and statistical modeling techniques
• Experience with data visualization tools such as Tableau or Power BI
• Excellent communication skills to convey complex findings to non-technical audiences

Data scientists look at patterns in company data to help teams make smarter decisions about everything from product design to customer outreach. Companies rely on their ability to spot opportunities and avoid costly mistakes that their competitors might miss.

4b. AI Data Scientist

AI data scientists represent the next evolution in data science, specializing in artificial intelligence applications and advanced machine learning techniques. While traditional data scientists work with various analytical methods, AI data scientists focus specifically on developing and implementing AI-powered solutions like natural language processing, computer vision, and generative AI systems.

On a typical day, an AI data scientist might fine-tune large language models, develop computer vision algorithms for image recognition, or build recommendation systems using deep learning. They work closely with AI engineers and product teams to deploy intelligent systems that can automate complex decision-making processes and enhance user experiences.

Salary: \$147K–\$203K/yr (Glassdoor)

Responsibilities

• Design and implement AI models for natural language processing, computer vision, and speech recognition
• Fine-tune pre-trained AI models and large language models for specific business applications
• Develop generative AI solutions using frameworks like GPT, DALL-E, and other foundation models
• Collaborate with data engineers to build AI-powered data pipelines and real-time systems
• Evaluate AI model performance and implement continuous improvement strategies

Key Skills

• Advanced proficiency in Python and specialized AI libraries (Transformers, Hugging Face, OpenAI API)
• Deep expertise in machine learning frameworks like TensorFlow, PyTorch, and scikit-learn
• Understanding of transformer architectures, neural networks, and deep learning principles
• Experience with AI platforms and cloud services (AWS SageMaker, Google AI Platform, Azure AI)
• Knowledge of prompt engineering, model fine-tuning, and AI ethics principles

Companies are discovering they need data scientist artificial intelligence specialists as AI becomes a bigger part of how they operate. Their AI skills let companies build systems that handle routine work automatically, customize experiences for each customer, and spot patterns that predict what's coming next.

5. Machine Learning Engineer

If you're fascinated by the potential of machine learning to transform industries, a career as a Machine Learning Engineer could be an excellent fit. While data scientists often focus on research and experimentation, Machine Learning Engineers are more concerned with the practical implementation of machine learning solutions, taking theoretical data science models and turning them into production-ready applications.

On a typical day, a Machine Learning Engineer might be working on tasks like integrating external datasets to enhance model performance, building APIs to make models more accessible to end-users, or implementing feature transformations to optimize model accuracy. It's a hands-on role that requires a blend of strong technical skills and creative problem-solving.

Salary: \$125K–\$196K/yr (Glassdoor)

Responsibilities:

• Designing and developing efficient, scalable machine learning systems
• Implementing machine learning algorithms to solve real-world problems
• Conducting tests and experiments to monitor and improve model performance
• Optimizing machine learning systems for production environments

Key Skills:

• Proficiency in programming languages like Python and C++
• Deep understanding of popular machine learning frameworks (e.g., TensorFlow, PyTorch)
• Experience with data structures and algorithms for building efficient models
• Familiarity with cloud platforms that support machine learning operations

As more companies look to leverage machine learning, the demand for skilled Machine Learning Engineers will only continue to grow. If you're ready to tackle complex algorithmic challenges and build intelligent systems that drive smarter decisions, this could be the ideal data science career path to pursue.

6. Deep Learning Engineer

Deep Learning Engineers are the professionals behind advanced AI systems that can learn and make decisions like humans. While data scientists work with all kinds of data, Deep Learning Engineers focus specifically on building complex models using deep neural networks.

What sets Deep Learning Engineers apart is their expertise in cutting-edge machine learning techniques.

They spend their days constructing sophisticated learning systems, fine-tuning algorithms to perfection, and working with teams to put these AI applications into action.

Salary: \$116K–\$188K/yr (Glassdoor)

Responsibilities:

• Building machine learning models that can recognize images and voices
• Optimizing algorithms so models run faster and better
• Teaming up with data scientists and engineers to launch models
• Always learning about the newest deep learning tech

Key Skills:

• Coding like a pro in Python
• Knowing deep learning tools like TensorFlow and PyTorch inside out
• Understanding the ins and outs of neural networks and algorithms
• Solving tough problems and collaborating with others

These engineers work on getting computers to do things we never thought possible, like recognizing faces or understanding speech. They're the ones figuring out how to make AI systems more powerful and useful for everyday applications.

7. Business Intelligence Developer

Business Intelligence (BI) Developers are able to transform raw data into powerful insights that drive smart business decisions. Rather than predicting the future, they focus on analyzing historical information to clearly show how a company has been performing. By thoroughly examining the data, they uncover actionable insights leaders can use to guide their strategies.

On a typical day, a BI Developer might be found defining requirements for BI tools, creating in-depth reports, or constructing sophisticated data models. It's all about ensuring the data is accurate, well-organized, and ready to inform those important business choices.

Salary: \$105K–\$164K/yr (Glassdoor)

Responsibilities:

• Design and build BI solutions tailored to the company's specific needs
• Maintain high data integrity and reliability across all platforms
• Develop user-friendly BI and analytics tools for easy data access
• Optimize BI tool performance based on user feedback

Key Skills:

• Expertise in BI tools such as Power BI, Tableau, or QlikSense
• Strong command of SQL and database management
• Data modeling abilities to support effective BI solutions
• Skill in translating complex data into clear, concise reports

Companies are realizing they need BI developers to turn their mountains of data into useful reports and insights. The demand for these skills keeps growing as more businesses want to make decisions based on actual data rather than guesswork. It's a solid career path for people who enjoy analyzing information and seeing their work directly influence business decisions.

8. Data Translator

Data Translators are a key part in helping organizations make data-driven decisions. They bridge the gap between the technical aspects of data science and the practical needs of the business.

While data scientists focus on building complex analytical models, Data Translators ensure those insights are understood and acted upon. They work closely with both technical teams and business stakeholders to align data projects with strategic goals.

On a typical day, a Data Translator might meet with data scientists to discuss their latest findings, then prepare reports explaining the business implications to non-technical colleagues. They are the link that enables data to power meaningful business decisions.

Salary: \$62K–\$108K/yr (Glassdoor)

Responsibilities:

• Align data science and business goals
• Facilitate communication between technical and non-technical teams
• Translate complex data insights into actionable strategies
• Manage end-to-end analytics initiatives to ensure business impact

Key Skills:

• Strong data analysis and interpretation abilities
• Excellent communication and stakeholder management skills
• Proficiency with data visualization tools like Tableau and Power BI

As organizations become more data-driven, the demand for skilled Data Translators will continue to rise. These professionals enable companies to maximize the value of their data investments.

9. Data Privacy Officer

Data Privacy Officers (DPOs) are important in safeguarding an organization's data and ensuring compliance with evolving privacy regulations. While data scientists analyze data for insights, DPOs focus on protecting information and adhering to legal standards.

DPOs possess extensive knowledge of privacy laws and excel at translating complex regulations into actionable policies. They actively monitor data practices, assess privacy risks, and update guidelines to align with changing requirements.

On a typical day, a DPO may review data handling procedures, conduct impact assessments, develop privacy policies, and educate employees on proper data practices. They also respond swiftly to address any data breaches or privacy concerns that arise.

Salary: \$103K–\$192K/yr (Glassdoor)

Responsibilities:

• Develop and implement comprehensive data privacy policies for the organization.
• Ensure compliance with GDPR, CCPA, and other relevant privacy laws.
• Conduct regular assessments to identify and mitigate privacy risks.
• Train employees on proper data handling practices.
• Promptly address and resolve data breaches or privacy issues.

Key Skills:

• Deep understanding of national and global privacy regulations.
• Ability to assess risks and identify potential security vulnerabilities.
• Technical expertise to implement data protection measures.
• Strong communication skills to explain legal requirements across the organization.

Companies are realizing they need Data Privacy Officers as they deal with more customer data and stricter privacy laws. They're the ones making sure companies can use data responsibly without getting hit with massive fines or losing customer trust.

10. AI Engineer

As artificial intelligence continues to transform industries, AI Engineers are the ones who design and deploy AI-driven solutions that enable businesses to automate processes and make smarter decisions. While AI data scientists focus more on developing and fine-tuning AI models, AI Engineers concentrate on the broader engineering infrastructure needed to deploy and scale these systems across an organization.

On a typical day, an AI Engineer might develop new machine learning models, optimize existing algorithms for better accuracy, and deploy AI solutions into production environments. They frequently collaborate with software developers, data scientists, and product teams to ensure AI systems integrate seamlessly with existing workflows, providing meaningful improvements to business performance.

Salary: \$107K–\$173K/yr (Glassdoor)

Responsibilities:

• Design and implement machine learning models and generative AI systems.
• Optimize and refine AI algorithms for real-world applications.
• Develop APIs and integrate AI solutions into existing software platforms.
• Evaluate model performance and make iterative improvements based on data-driven feedback.
• Collaborate cross-functionally to deliver AI-driven projects aligned with strategic goals.

Key Skills:

• Advanced proficiency in programming languages such as Python, Java, or C++.
• Expertise in machine learning frameworks like TensorFlow, PyTorch, and scikit-learn.
• Strong mathematical foundation in statistics, calculus, and linear algebra.
• Ability to deploy and manage machine learning models in cloud environments (AWS, Azure, or GCP).
• Excellent problem-solving and analytical thinking skills for complex technical challenges.
• Strong communication skills for collaborating with cross-functional teams and stakeholders.

As AI continues its rapid expansion into industries ranging from technology and finance to healthcare and retail, AI Engineers will be at the heart of developing the smart tools and systems of tomorrow. Without their technical know-how, most AI projects would stay stuck in the research phase instead of helping real customers.

How to Prepare for These Roles

Want to excel in data science? Focus on three key areas: technical skills, practical projects, and continuous learning. Here's how to set yourself up for success in this field.

A: Build Strong Technical Skills

First, become proficient in programming languages commonly used in data science, such as Python and R. Next, learn how to clean data, create visualizations, and implement machine learning algorithms. Developing these abilities will allow you to tackle complex datasets and perform the advanced analyses required in data science roles.

B: Showcase Your Skills Through Projects

One of the best ways to demonstrate your capabilities to potential employers is by building an impressive portfolio. Look for opportunities to take on personal projects where you analyze real-world datasets. You can also contribute to open-source initiatives. This hands-on experience not only hones your skills but also highlights your ability to apply theoretical knowledge in practice

C: Commit to Lifelong Learning

Data science is a rapidly evolving field, so staying up-to-date is important. Engage with professional communities, attend relevant conferences, and tap into online learning resources. These activities will give you valuable insights into current industry practices and future directions. Remember, continuous skill development is key to thriving in data science.

In summary, preparing for a data science career involves a well-rounded approach. By building strong technical skills, applying them through practical projects, and embracing lifelong learning, you'll be well-equipped for success. As data science continues to advance, these strategies will help you thrive both now and in the future.

How to Choose the Best Data Science Role for You

Choosing the best data science job comes down to knowing your strengths, skills, and what each job requires. This section will walk you through a self-assessment to help figure out which position might be your ideal match.

The Different Data Science Roles

Data science includes several distinct roles, each with its own set of responsibilities and necessary skills:

• Data Engineer: Builds infrastructure and pipelines for collecting, storing, and processing data.
• Data Scientist: Examines complex data to uncover insights, make predictions, and develop strategies.
• AI Data Scientist: Specializes in data scientist artificial intelligence applications, focusing on AI model development, natural language processing, and generative AI solutions.
• Machine Learning Engineer: Creates algorithms and predictive models using big data.
• Data Architect: Designs the blueprints for an organization's data management systems.

Traditional vs. AI Data Science Paths

When considering your career direction, think about whether you're drawn to traditional data science or the specialized AI data scientist track. Traditional data scientists work across diverse analytical methods and business problems, while AI data scientists focus specifically on artificial intelligence applications like machine learning models, natural language processing, and computer vision. Your choice depends on your interest in AI technologies and your willingness to specialize in this rapidly evolving area.

Assessing Your Fit for a Data Science Career

To determine if a data science career is right for you, ask yourself:

• Are you passionate about continuous learning and problem-solving?
• Do you feel comfortable with programming languages and data analysis tools?
• Can you effectively communicate complex data insights?
• Are you persistent enough to tackle difficult data challenges?
• How well do you collaborate on projects with different teams?

Your answers to these questions can provide valuable insight into which data science role best aligns with your abilities and interests. Top data scientists have a blend of technical skills, analytical capabilities, and personal qualities like curiosity and a keen eye for detail. Specialized machine learning skills are also predicted to be in high demand for these positions.

Aligning Your Career for Satisfaction

It's important to match your personal traits with your professional goals. Making sure your abilities fit the requirements of a specific role not only boosts job satisfaction but also enables career growth. So whether you're a student hoping to enter the field or a professional looking to advance, understanding these alignments is key.

In summary, taking time to reflect on your strengths and objectives can significantly help in identifying the data science role that's the right fit for you. Choosing a career path well-suited to your skills and interests will put you on the road to success and fulfillment in this field.

At the End of the Day...

Data science roles are shaping the future of business and technology. In this article, we explored 10 high-demand data science jobs that are making a big impact. From data engineers to AI data scientists, these positions offer diverse and rewarding career paths.

If you're ready to launch your data science career, a strong educational foundation is key. Here's how to get started:

Kickstart Your Data Science Career

• Begin your journey with Dataquest's Data Scientist in Python career path
• Explore advanced techniques with the Machine Learning in Python skill path
• Apply your skills to real-world data science projects to showcase your expertise to employers

As data science continues to evolve, adaptability and continuous learning are important for staying competitive. By honing your skills with Dataquest's interactive courses and hands-on projects, you'll be well-prepared to thrive in the field of data science.