Top 10 Data Science Roles to Look Out for in 2025

Job hunting can surely be exhausting and stressful; after all, it is a task that comes with many ups and downs. It is even more of a hassle in a field like data science, which is rapidly growing not only in the tech world but also in other industries. The plethora of roles under the data science umbrella also baffles and confuses people into applying for a data science role that may not necessarily resonate aptly with their skill set. Consequently, many people striving to pursue a career in analytics are unsure where to start.

In this blog, we have assembled different data science job roles that aim to help experienced professionals and freshers alike understand the nature of the work that these roles entail.

Data science job roles

Data science teams are responsible for solving complex problems with the help of - you guessed it - data. Even though, for the most part, roles under the data science umbrella require data to work with, the necessary skills and tools can vary considerably.

This is an exhaustive list of the top 10 data science roles that are diverse and require a varied skill set. Go through them to understand what role fits best for you, but keep in mind that these data science job titles are not fixed and can vary depending upon the company and industry.

Now without any further ado, let's have a look at the list of data science roles -

1. Data Scientist
2. Data Analyst
3. Data Engineers
4. Data Administrator
5. Machine Learning Engineer
6. Data Architect
7. Statistician
8. Business Analyst
9. Data and Analytics Manager
10. Technology specialized roles

1. Data scientist

The first role that comes to mind whenever we think of data science is of a data scientist. The most general role can also be the jack of all trades. Thereby, to advance in your career as a data scientist, you must understand all the business aspects. You should be able to offer the best solution to business problems by implementing processes, right from collecting and analyzing to visualizing and presenting data.

Here's a data scientist job description that lists all the typical tasks a professional working in this role is expected to perform.

a. Roles and responsibilities

• Gathering and analyzing data from various trustable sources
• Processing, cleansing, and integrating data to derive insights
• Utilizing tools and techniques to visualize data and help design future strategies
• Suggesting alternative methods and proposing solutions to tackle complex business challenges

b. Required tools/skills

• Proficiency in languages like R or Python and query languages such as SQL, Hive, Pig, etc., is needed.
• Good knowledge of machine learning algorithms and applied statistical skills are desirable.
• Strong hold over data visualization tools like Matplotlib, ggplot2, D3.js, Tableau, etc.
• Excellent written and verbal communication skills are required.

c. Top companies

Top companies that have hired data scientists- Deloitte, PwC, Amazon, Microsoft, etc.

2. Data analyst

The data analyst role sometimes overlaps with the data scientist role due to the similarities that both roles exhibit. A data analyst’s job entails various tasks, including optimizing, visualizing, transforming, and manipulating the data.

a. Roles and responsibilities

• Utilizing automated tools for gathering data from various sources.
• Optimizing gathered data and ensuring their accuracy and quality.
• Performing data analysis, maintaining databases, deriving insights, and creating detailed reports.
• Promoting data literacy by collaborating with different departments and stakeholders.

b. Required tools/skills

• A keen eye for detail and the ability to assess potential risk.
• Ability to process large data sets and transform them into beneficial insights.
• Proficiency in ETL tools, Hadoop-based analytics, and business intelligence concepts.
• Strong hold over data visualization, data warehousing, and data retrieval concepts.

c. Top companies

The top companies hiring for data analyst roles are - ScienceSoft, SG Analytics, Tableau, Sisense, etc.

3. Data engineers

The role of a data engineer is crucial for the operation of a data science team. A data engineer's job necessitates developing, testing, and maintaining big data ecosystems optimal for the business and data scientists to run their algorithms.

a. Roles and responsibilities

• Developing and aligning the database with the business needs.
• Building algorithms and prototypes to transform data into insights.
• Creating data systems and pipelines that make reporting, analysis, and using data more accessible.
• Proposing improvements to enhance data quality and reliability.

b. Required tools/skills

• Proficiency in languages such as Java, Python, R, etc.
• Stronghold over relational and non-relational databases and ETL tools.
• Technical expertise in data storage, automation, and scripting.
• Hands-on experience with machine learning concepts, big data tools, and cloud computing.
• Strong knowledge of data security concepts and practices.

c. Top companies

The top companies hiring for data engineering roles are - Facebook, Airbnb, AT&T, Capital One, etc.

4. Database administrator

A database administrator's job involves responsibility for directing or performing activities to maintain and secure a thriving database environment. They ensure that the stored data is reliable, error-free, and available. This role also necessitates apt backups and ways to recover data quickly and accurately in the event of failure.

a. Roles and responsibilities

• Designing, developing, and monitoring database environment.
• Identifying problems at the right time and resolving them to avoid significant bottlenecks.
• Implementing strict security measures and backup procedures.
• Proposing improvements to refine the database system.

b. Required tools/skills

• Proficiency in MS SQL, Oracle Database, Hadoop, or PostgreSQL technologies.
• Ability to set up, maintain, and monitor data networks.
• Hands-on experience with data backup, recovery, security, and integrity.
• Excellent analytical and problem-solving skills.

c. Top companies

The top companies hiring for database administrator roles are - IBM, TCS, Oracle, AT&T, Bank of America, etc.

5. Machine learning engineer

Machine learning engineers are highly skilled professionals who work with data scientists and data analysts to perform A/B testing, build data pipelines, and implement machine learning techniques such as classification, clustering, etc., to predict trends and patterns. The ultimate goal of individuals working in this data science role is to eventually create self-running artificial intelligence to automate predictive models.

a. Roles and responsibilities

• Developing machine learning systems and implementing suitable ML algorithms.
• Building data pipelines, running tests and performing statistical analysis to predict accurate test results.
• Monitoring and assessing performance and reliability of the machine learning systems and refining them as required.
• Training and deploying machine learning models.

b. Required tools/skills

• Proficiency in languages such as R, Python, or Java.
• Extensive experience and knowledge of machine learning frameworks/libraries like PyTorch, Tensorflow, Flux, etc.
• Strong foundation in mathematics and statistical analysis.
• Knowledge of Elasticsearch, SQL, Amazon Web Service, and REST APIs.

c. Top companies

The top companies hiring for ML engineer roles are - Amazon AWS, Databricks, IBM, etc.

6. Data architect

Data engineers and data architects are closely related positions. These data science roles ensure that data scientists and analysts have well-formatted and accessible data to work with. Data architects’ job requires them to formulate the organizational data strategy, including data quality standards, data flow, and security measures.

a. Roles and responsibilities

• Developing and implementing data strategies that are in line with the business objectives.
• Supervising and participating in end-to-end data architecture, right from design to implementation.
• Overseeing data migration strategies and implementation.
• Ensuring optimal efficiency and security for database systems.

b. Required tools/skills

• Proficiency in database structure principles, data mining, and segment techniques.
• Expertise in technologies such as SQL, Hive, Pig, Spark, etc.
• Hands-on experience with concepts such as data warehousing, data modeling, ETL, etc.
• Proven analytical, problem-solving, and communication skills.

c. Top companies

The top companies hiring for data architect roles are - Northrop Grumman, IBM, Oracle, Abbott, etc.

7. Statistician

Primary forms of statistics have always been a part of our civilization which means we can consider this data science role as the historical leader of data and insights. As the name suggests, a statistician is an expert that works with theoretical or applied statistics. It is quite common to combine the expertise of statistics with other fields. In this case, we have organizational data.

a. Roles and responsibilities

Analyzing and interpreting numerical data to derive actionable insights.
Performing tests and ensuring the reliability and quality of the data.
Assisting in decision-making by refining business strategies.
Communicating the results or findings with the stakeholders.

b. Required tools/skills

Proficiency with tools such as SPSS, SAS, or Stata.
Stronghold over statistical techniques, formulas, calculations, and logic.
Ability to leverage algorithms and other technologies to manipulate data.
Excellent communication skills to effectively communicate their analysis with other team members.

c. Top industries

The top companies hiring for data statistics roles are - IQVIA, Merck, Johnson & Johnson, Apple, Abbott, etc.

8. Business analyst

Unlike other data science roles, business analysts’ job demands them to work with business operations and information technology (IT) teams to process, analyze, and document business data for procedures, products, or services to extract actionable business insights for business growth.

a. Roles and responsibilities

• Understanding all the aspects of business and conducting thorough research and analysis to improve processes and systems.
• Liaising with different departments and stakeholders to gather information and deliver logical conclusions.
• Developing innovative solutions to complex business issues in line with operational and strategic improvements.
• Allocating resources, budgeting, and forecasting.

b. Required tools/skills

• Proficiency in business operations, business intelligence, and excellent tech expertise.
• Expert knowledge of project management tools, Microsoft Office Suite, etc.
• Sound knowledge of statistical methodologies.
• Stronghold over analytics and conceptual thinking.
• Ability to deliver accurate results.

c. Top companies

The top companies hiring for business analyst roles are - Cisco, Accenture, Capital One, IBM, etc.

9. Data and analytics manager

A data and analytics manager oversees and sets the direction for the entire data science team. They delegate tasks to the team, assign priorities, and design the processes that support a positive outcome.

a. Roles and responsibilities

• Supervising the data science operations and leading the data science team.
• Designing and implementing data analysis strategies and procedures.
• Recruit and train junior team members.
• Ensuring all the data-driven initiatives are successfully implemented.

b. Required tools/skills

• Capable of handling multiple data-driven projects.
• Prior experience working with business intelligence tools, software, and dashboards.
• Proficiency in Java, C, C++, SQL, SAS, etc.
• Firm grasp of tools such as Teradata, Aster, Hadoop, Tableau, Adobe Analytics, etc.

c. Top industries

The top companies hiring for data and analytics manager roles are - Amazon, IBM, Google, Microsoft, etc.

10. Technology specialized roles

Data science is still evolving, and as it grows, so do the roles that require specific technical expertise. A few examples of such jobs are AI specialists, Deep Learning specialists, NLP specialists, etc. Such data science roles shrink the perimeter of the general data science workload and enable the professional to focus on one specific technology.

How to land the right data science job?

After going through the list of these diverse data science positions, you must have decided to push your data science career. But, even after determining the role you want to grow in, it is not an easy task to land the right data science job.

What’s the solution?

The solution is Turing.com. It is an AI-driven deep jobs platform that enables you to apply for remote jobs at US-based companies from the comfort of your home. So, if you’re looking to get full-time, long-term remote data science jobs with better compensation and career growth, apply for Turing jobs today!

FAQs

i. Why is data science in demand?

Ans. Data proliferation has led to businesses across industries demanding professionals who can monitor, manage, and collect data and gather insights to measure performance in order to enhance decision-making across the organization.

ii. Is data science a good career?

Ans. According to the US Bureau of Labor Statistics, jobs that require data science skills are projected to grow by 27.9% by 2026. In fact, the entire market size is expected to grow from USD 95.3.9 billion in 2021 to USD 322.9 billion by 2026. Such highly positive statistics prove that data science is an extremely good career option that offers tremendous opportunities, perks, and competitive salaries.

iii. Where do data scientists make the most money?

Ans. According to the US Bureau of Labor Statistics, the top industries that demand and pay handsomely for highly skilled data scientists are - aerospace, finance, computer systems design, technological consulting, and scientific research.