Top 13 Data Science Job Roles for 2025: Career, Skills and Salaries

Introduction

The IT industry has been revolutionized or rather transformed through Data Science and the demand is increasing. The demand for qualified specialists in 2024 is higher than ever, and it should grow by 36% by 2030. With more companies enhancing the use of data solutions, fresh opportunities that offer good pay and growth prospects are being opened in this sector.

Those who are considering a career change or are majoring in Data science, there can be no better time as this, to know about the modern trending data science jobs.

Here’s the list of the top 13 data science roles you can aim at in 2025 based on the required skills, responsibilities, and surprisingly high average salaries.

Junior Level Roles

1. Data Analyst

Working as a data analyst in any data driven organisation is important because you play a major role of transforming raw data to actionable insights. They collect, prepare and analyze data and do so in order to identify trends, solve problems, and aid in decision making. However, this provides evidence based recommendations for stakeholders to drive success.

Key Responsibilities of the Role:

• Data Collection and Processing: Collect data for analysis and involve data cleaning process.
• Trend Analysis: Understand trends that are used to justify business performance and the direction of strategy.
• Reporting: When reporting use Tableau and Power BI to develop clean and transparent dashboards and reports.
• Decision Support: Give suggestions and proposal to facilitate the advancement of business strategies.

To qualify for this position, candidates should hold a bachelor's degree in one of the following fields:

• Information Technology
• Data Analytics
• Business

Required Skills:

• Data Visualization: The act of computerizing data and transforming it into graphical representation using Tableau or PowerBI.
• Python Programming: Someone who familiar with python and its libraries for different data analysis tasks like data manipulations in Pandas, NumPy.
• Statistical Analysis: Methods of basic statistical analysis and for testing hypotheses.
• SQL Skills: High level of proficiency in performing data manipulation and getting information with SQL.

Experience Level:

For fresh graduate / 2 years or less experienced candidates in this area.

Salary: India: ₹4-7 Lacs Per Annum | The United States: $60-$80K Yearly

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2. BI Developer

A BI Developer creates different systems and dashboards which, working on raw data, give business companies options for better decision making. They create pipelines, models and reports as well as work with the teams, to achieve the business targets effectively.

Key Responsibilities of the Role:

• BI Solution Development: Design and develop BI solutions such as data warehouses, ETL processes, and reporting systems.
• Dashboard Creation: Create appealing and informative visuals on Power BI or Tableau.
• Data Integration: Gather and arrange data during information searching and storage for analysis.
• Performance Optimization: Optimize database queries and BI tools for faster performance and scalability.
• Collaboration: Work closely with business analysts and stakeholders to identify solutions that will be best suited for business.

To qualify for this position, candidates should hold a bachelor's degree in one of the following fields:

• Computer Science
• Information Systems
• Data Analytics or a related field.

Technical Skills:

• BI Tools: Coding skill to create dashboard in Power BI or Tableau or similar tool Skill.
• SQL: Proficient in both writing and how to write queries when it comes to data analysis.
• ETL: Knowledgeable in data integration in pipeline tools.
• Data Modeling: Suitable for constructing complex and optimized data architectures capable of supporting thousands and millions of users.

Required Skills:

• Data Visualization: Expertise in creating compelling visualizations that clearly convey business insights.
• Analytical Mindset: Strong analytical skills to interpret data and identify trends, anomalies, and actionable insights.
• Problem-Solving: Ability to troubleshoot and resolve technical issues in BI solutions.
• Communication: Strong communication skills to collaborate with both technical teams and non-technical stakeholders.

Experience Level:

Mid-level position, typically requiring 2–4 years of experience in BI development or related fields.

Salary: India: ₹8–12 LPA | The United States: $80K–$110K per annum.

3. Business Intelligence Analyst

A BI Analyst converts data into strategic insight, enabling businesses to make informative decisions. This role requires expertise in Data Analysis, Business Strategy & solution development making it essential for organizations seeking a competitive edge.

Key Responsibilities of the Role:

• Data Analysis & Reporting: You will analyse, gather, and then interpret business data to create complex dashboards and reports.
• Stakeholder Collaboration: Finalize key metrics and performance indicators together with stakeholders.
• Strategic Alignment: Meet the business objectives and goal of the organization by aligning data driven strategies.

To qualify for this position, candidates should hold a bachelor's degree in one of the following fields:

• Business
• Computer Science
• Data Analytics

Knowledge of data visualization tools and database management is required.

Required Skills:

• BI Tools Expertise: The ability to handle tools, such as Tableau, Power BI, or Looker.
• SQL: Writting and optimizing SQL queries experience.
• Analytics Knowledge: Knowledge of Python or R in data analysis.
• Communication: Strong skills presenting to both technical and non technical teams.

Highly knowledgeable in organizing and archiving data for provision of ease in data analysis.

Experience Level:

This is an entry-level position, and hence suitable for candidates with experience of 0–2 years, or candidates who have done a 0–2 years internship, or 0–2 years course work in business intelligence tools.

Salary: India: ₹4 – ₹8 LPA | The United States: $55,000 – $75,000.

So why pursue a career in business intelligence?

BI Analysts are at the frontline of making data-backed decision. As businesses today are getting more data-oriented to stay ahead of their competitors, the demand for skilled BI Analysts continues to grow.

4. Data Engineer

A Data Engineer designs and implements processes and platforms for data exploitation with particular focus on their effectiveness in terms of performance, storage space and scalability. They build robust data processes, securing high quality data and build solutions for use cases such as analytics and machine learning.

Key Responsibilities of the Role:

• Data Pipeline Development: Design and implement data pipelines from multiple sources: extraction, transformation and load (ETL) data to data warehouses.
• Data Warehousing: Building and deploying scalable data storage solutions to relational (SQL) and non-relational (NoSQL) databases.
• Data Quality Management: Managing and monitoring integrity of the data. Have reliable data that is of good quality and which is available for proper analysis and information reporting.

To qualify for this position, candidates should hold a bachelor's degree in one of the following fields:

• Data Engineering
• Computer Science
• Software Engineering
• Information Technology

Proficiency with the Python, Java, Scala programming languages, and the fundamentals of databases or PostgreSQL, MySQL and NoSQL including mongoDB, Cassandra.

Required Skills:

• ETL Process: Integration of data by doing ETL workflow Developing, managing ETL workflows.
• Big Data Technologies: Viability of doing polymerization in Hadoop, Spark, or Kafka for the purpose of large scale big data processing.
• Cloud Platforms: Experience with data storage and processing in the cloud through services (AWS, Azure, GCP).

Experience Level

Experience in data engineering and infrastructure building, mid-level, 3-5 years.

Salary: India: ₹10-15 LPA | The United States: $100-130K per year

Data Engineers are the backbone of the modern, data-driven organization since they play an important role in developing advanced analytics and business intelligence decisions with underlying systems.

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5. Database Administrator

The main function of a Database Administrator is to manage a database, so it can be secure, available, and executed well. This role requires detail-oriented people with the skills to optimize and troubleshoot systems.

Key Responsibilities of the Role:

• Database Design & Development: Design and develop robust database structures to fulfil the organisational nutritional requirements.
• Backup & Recovery: Back up the database on a regular basis, and implement procedures that restore the database if it should suffer a loss of data.
• Performance Optimization: Observe database performance, and optimize database queries for speed and high efficiency.
• Data Security: Take measures to protect sensitive data and avoid security breaches.

To qualify for this position, candidates should hold a bachelor's degree in one of the following fields:

• Computer Science
• Information Technology or a related field

The mid-level DBAs should know in and out of database management systems and server architecture.

Required Skills:

• Database Management: Database system proficiency in MySQL, Oracle, or SQL Server along with SQL knowledge; database backup and recovery processes.
• Cloud Database Services: Databases experience on cloud platforms like AWS RDS or Azure SQL.
• Scripting Languages: Experience with scripting languages like Python or Shell for automating tasks and database operations.
• Database Security: Protocol and best practice knowledge around how to secure and comply with your databases.

Experience Level

They should have between 3–5 years of experience of database management with hands-on exposure to complex database environments.

Salary: India, mid-level Database Administrators are getting paid ₹8–15 LPA. The United States is paying in the ballpark of $80,000–$120,000.

Why Pursue a Career as a DBA?

DBAs are critical to businesses that have a lot at stake with regards to data. On hand is a mix of technical challenges, job security and possibilities in the advancement into more senior roles like database architect or cloud database specialist.

6. Data Storyteller

This is a person who takes complex data and turns it into compelling and understandable story. As these professionals have a knack for combining their creativity with technical data skills to affect business decisions and strategies this makes them the perfect fit for this position.

Key Responsibilities of the Role:

• Data Visualization: Be able to design and create impactful visualizations that call out key takeaways of the data.
• Collaboration: Deliver the data driven findings into understandable insights by working closely with data scientists and analysts.
• Simplification: Breakdown complex data into writeable text and visual formats for audiences that are not technically minded.
• Communication: Allows for easier understanding of insights in data through story telling techniques and user friendly visuals.

To qualify for this position, candidates should hold a bachelor's degree in one of the following fields:

• Computer Science
• Information Technology or a related field

They prefer mid-level professionals who have experience in data visualization tools and analytics platforms and also have knowledge of data analysis techniques.

Required Skills:

• Technical Proficiency: Data visualization tools like Tableau or Power BI, as well as programming languages like SQL, Python or R.
• Data Storytelling: The ability to craft compelling narratives from data insights to engage, and report to, stakeholders.
• Machine Learning: Most of the times proficiency in machine learning concepts to boost the data analysis and prediction capability is worth it (optional but helpful).
• Business Intelligence Tools: Familiarity with BI software that drives the data driven decision making process.

Experience Level

Mid-level Data Storytellers have 3-5 years of experience in data analytics careers and/or visualization.

Salary: India ₹8–15 LPA | The United States $80,000 to $120,000 per year.

Why should one become a Data Storyteller?

Data storytelling is an emergent career in data science work that empowers people to affect business decisions through an intuitive and perky narrative based on data. This is a really cool mix of technical skills, creativity, and impact, and it is one cool career if you’re really passionate about data and communication.

7. Machine Learning Scientist

Machine Learning Scientists create algorithms that allow computers to learn from and predict or make decisions based on data. People who are passionate about moving artificial intelligence forward and solving hard problems by modeling with data: these are exactly the people who would be perfect for this role.

Key Responsibilities of the Role:

• Model Development: Use machine learning models to design, develop, and test to tackle real world problems.
• Algorithm Optimization: Try and optimize algorithms for improved performance and scaling.
• Collaboration: Contribute to scalable, efficient machine learning solutions in close collaboration with data scientists.
• Research & Innovation: Find out what is going on in the field and inspire technological.

To qualify for this position, candidates should hold a master's or Ph.D. degree in one of the following fields:

• Computer Science
• Information Technology or a related field

The job of the professional in this role should be to have a deep understanding of machine learning algorithms, data structures and statistical models.

Required Skills:

• Algorithm Expertise: Strong understanding of machine learning algorithms and their applications.
• Programming Proficiency: Fluency in programming languages such as Python, R, or Java.
• Data Analysis: Experience in data handling, analysis, and application of statistical methods.
• Deep Learning Frameworks: Proficiency with TensorFlow and PyTorch for building advanced models.
• Cloud Platforms: Familiarity with cloud services to deploy and scale machine learning solutions.
• Reinforcement Learning: Knowledge of reinforcement learning is an added advantage.

Experience Level:

Machine Learning Scientists are expected to have a minimum average of 3 to 5 years of experience developing and deploying machine learning models in data analysis jobs.

Salary: India: ₹12 and ₹25 LPA | The United States: $100,000 to $150,000 a year.

What makes this role so great?

Machine learning occupies the vanguard of technological innovation and has great potential to advance a career. In this job, you will be working as a Machine Learning Scientist building on the newest achievements in AI like those within healthcare, finance and autonomous systems.

8. Data Scientist

A Data Scientist solves problems by using data analytics and statistical modeling to drive informed business decisions. Their MO is to combine analytical skills, domain knowledge, and technical expertise to analyse complex datasets and make them do even more for the (business or organization).

Key Responsibilities of the Role:

• Data Analysis: Pattern, trend, and insight must be found from large and complex datasets one has to analyse.
• Statistical Modelling: statistical techniques such as machine learning algorithms for predictive models
• Generate Insights: Converts data insights into actionable business strategy and operation recommendations.

To qualify for this position, candidates should hold an Advanced degree in one of the following fields:

• Data Science
• Statistics
• Mathematics or other related disciplines

Strong foundation in Python, R, and mastery skills in data manipulation and statistical analysis.

Required Skills:

• Machine Learning & Predictive Analytics: Solid understanding of machine learning, predictive analytics, and data visualization techniques.
• Programming Skills: Strong proficiency in Python or R for data analysis, model building, and algorithm implementation.
• Statistical Analysis: Deep knowledge of statistical methods, hypothesis testing, and predictive analytics for accurate data interpretation.
• Algorithm Expertise: Experience with algorithms used for classification, clustering, and regression tasks.

Experience level

Mid-level: 3 to 5 years of experience in data science and analytics

Salary: India: ₹15-25 LPA | The United States: $130-180K per year

Data scientists are necessary in bridging an organization's strategic decision with data power. Combining analytical depth with business sense, they make teams go.

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9. Machine Learning Engineer

A Machine Learning Engineer is responsible for the technical implementation of algorithms that permit systems to adapt to new data. They create high performing predictive models and start machine learning solutions to solve issues and improve product/service offerings.

Key Responsibilities of the Role:

• Model Design & Tuning: Machine learning models classification, regression, and recommendation systems design and fine tune.
• Algorithm Optimization: Use optimization techniques such as hyperparameter tuning and feature engineering to enhance model performance.
• ML Solution Deployment: Deploying Machine Learning Pipeline with high performance and reliability.

To qualify for this position, candidates should hold a M. Tech/Ph.D. in one of the following fields:

• Computer Science
• Computer Engineering or equivalent fields

Python with considerable hands-on experience in utilization of various machine learning libraries, i.e. TensorFlow and PyTorch. knowledge of Deep learning, model evaluation techniques, and core software engineering principles.

Required Skills:

• Python Development: Strong knowledge of Python for model development and software integration.
• Deep Learning: Knowledge of neural networks and deep learning frameworks (e.g. TensorFlow, PyTorch).
• Software Engineering: Building, testing, deploying scalable machine learning applications.
• Level of Experience: Mid-level, 3-5 years of experience, with machine learning and AI project experience.

Salary: India: 12-20 LPA | The United States: $120-160K per year

Machine Learning Engineers work on enhancing AI capabilities and, in so doing, assist in transforming businesses and products.

Senior Level Roles

10. ML Ops Engineer

Senior ML Ops Engineer is responsible for the entire lifecycle of machine learning models; how to deploy, how to scale, and how to monitor machine learning models in production environments. We believe this role is ideal for those with a deep background in machine learning and system engineering that are committed to optimizing workflows and delivering operational excellence.

Key Responsibilities of the Role:

• ML Model Deployment: Deploying machine learning models — Design, implement, and manage scalable systems for it.
• Pipeline Automation: Streamline operations by automating data ingestion, model training, deployment, and monitoring processes.
• Model Integration: Make Machine learning Models work properly with production systems to sustain seamless operation and performance.
• Collaboration with Data Scientists: Continuously improve model performance and optimize deployment while working closely with data scientists.

To qualify for this position, candidates should hold a Bachelor’s or Master’s in one of the following fields:

• Computer Science
• Information Technology or a related field

Experience with versions control systems such as Github or GitLab. Specific exposure to the machine learning algorithms and also model deploying experience.

Required Skills:

• Cloud Platforms & Containerization: Cloud platform experience and containerisation for machine learning knowledge.
• Programming Skills: Experience with Python, TensorFlow, and PyTorch for building and deploying machine learning models.
• CI/CD & DevOps: Understanding CI/CD pipelines, devops practices and data engineering tools to streamline workflow automation.
• Additional Skills: It’s a plus if you have experience with tools that help deploy and scale your machine learning models.

Experience Level:

They 're expected to have 5 or more years of experience with machine learning operations and systems engineering as Senior ML Ops Engineers.

Salary: India: ₹20-₹35 LPA | The United States: $120,000-$160,000 per annum.

What makes become an ML Ops engineer?

With more organizations implementing machine learning, an ML Ops Engineers work more closely to ensure the operationalization of these solutions. It has huge growth potential and presents great technical challenges to enhance Machine Learning functions in different fields.

11. Data Architect

A Data Architect is a senior position responsible for the design and execution of the organization’s data systems. They connect data systems with business needs to integrate secure, efficient, and effective data solution to enhance the organizational data process.

Key Responsibilities of the Role:

• Blueprint Development: Decide on how data should be stored, incorporated and analyzed.
• Data Modelling: Develop a successful storage and recognition system for optimum structure of data.
• Scalability and Performance: Figure out the issues of scalability so that the speed and data could be improved.
• Cloud Data Solution Management: AWS, Google Cloud, Azure, and similar are your ideal picks for scalable big data management and processing.

To qualify for this position, candidates should have:

• Knowledge of data modeling, design and database management
• Experience in AWS, Google Cloud or Azure
• Specialization in administration of large scale structure of data, security and efficiency.

Required Skills:

• AWS & Database Management: Prior experience as a Database Administrator on AWS and Professional level skills in SQL for Data management.
• Cloud Architecture: Awareness of cloud data solutions and services to create and implement architectural cloud solutions.
• Data Governance: Knowledge on how to implement; data quality and data privacy and data protection laws.

Experience Level:

Leading experience level. Senior level with more experience in data architecture and based on cloud solutions would be the result of this. Skills: experience SQL, cloud architecture, data governance, and experience level lead / senior.

Salary: India: ₹20 to ₹30 lakhs p.a | The United States: $ 150,000 to $ 200,000 p.a.

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12. Data Strategist

A Senior Data Strategist job involves the devising and deployment of data-driven methods to assist organisations in achieving the objectives of business undertakings. This job would suit any person who has a good eye for business and is well developed in the use of data in the making of business related decisions.

Key Responsibilities of the Role:

• Data Strategy: Coordinate and guide the lead data strategy organization with respect to its business objectives.
• Metrics: Conduct benchmarking to guide on organizational performance and make facts-based choices.
• Information Structuring: Make sure that the data is ordered properly to be used for decisions.
• Data Management: Supervise data solutions processes and make certain that they adhere to the polices.

To qualify for this position, candidates should hold a Bachelor's or Master’s degree with a major in one of the following fields:

• Business Analytics
• Data Science, or another major in a related field

Have expertise and background in data analysis, strategy creation, business intelligence, key data governance, and management.

Required Skills:

• Skills: Expertise in data management, data analysis, data strategy, reporting, and the use of business intelligence tools.
• Leadership & Communication: Strong leadership abilities and excellent communication skills to effectively engage stakeholders and lead teams.
• Good-to-Have Skills: Knowledge of Data Governance, awareness of cloud solutions, and experience in implementing data-driven changes to drive organizational improvements.

Experience Level:

The individual should be a Senior Data Strategist should have 7–10 years of experience in data strategy and analytics.

Salary: India: ₹20–40 LPA | The United States: $120,000 to $160,000 per annum.

Why a Career in Data Strategy?

This role provides an opportunity for persons to guide the management process of an organization based on evidence. Data Strategists are vital to deal with all these challenges which are associated with data management and the role is full of challenges which are potential to transform industries.

13. Data Product Manager

A Data Product Manager is responsible for introducing and managing data products that can serve end-users and be helpful to the company. They coordinate with technical people to work for business people in order to deliver useful products with benefits for business and customers.

Key Responsibilities of the Role:

• Product Requirements & Specifications: Specify the precise features of the product, its characteristics, and necessary facilities for the client in the process of its use.
• Team Management: Coordinate a multitude of teams (engineering, design, data science) to accomplish the product vision of the project.
• Continuous Product Improvement: Control the whole process of developing a product from the initial concept to delivering subsequent updates in response to customer opinions.
• Product Road mapping: Make sure of Product Road map that matches business strategy for better procurement and understanding with the other stake holders.

To qualify for this position, candidates should hold a B.S. / M.S. degrees in one of the following fields:

• Business Administration
• Data Science or equivalent, with background in statistics preferred.

Knowledge areas include data analysis and business intelligence—the use of data for decision-making with background in statistics preferred.

Required Skills:

• Business Strategy: To help in setting strategic goals for product development in line with organisational corporate goals.
• Data Analysis: He/she should excel in terms of data analysis and was involved in product decisions and hypothesising.
• Stakeholder Management: Effective communication when it comes to dealing directly with the various stakeholders such as the internal teams and partners, and the general outlook of having goals that are in line with the product on sale to the customers.

Experience:

Advanced experience above the senior level with a track record in data product management.

Salary: India: ₹ 25-35 LPA | The United States: $140-180K per year

Conclusion

It’s full of brilliant prospects high and low from high-tech and healthy to sound-on and sales. Whether your aim is just to begin your career, or to move it to the next level, you would be able to understand how all these different professions assist in managing one’s career path towards achievement.

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