Data Science Job Roles

shashank shukla
5 min readOct 27, 2020


Data Selection According To Role

1. Data Analyst:-

Data analysts are responsible for a variety of tasks including visualization, munging, and processing of massive amounts of data. They also have to perform queries on the databases from time to time. One of the most important skills of a data analyst is optimization. This is because they have to create and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data.


SQL, R, SAS, Python are some of the sought after technologies for data analysis.

2. Data Engineers:-

Data engineers build and test scalable Big Data ecosystems for the businesses so that the data scientists can run their algorithms on the data systems that are stable and highly optimized. Data engineers also update the existing systems with newer or upgraded versions of the current technologies to improve the efficiency of the databases.


Technologies that require hands-on experience include Hive, NoSQL, R, Ruby, Java, C++, and MATLAB.

3. Database Administrator:-

The job profile of a database administrator is pretty much self-explanatory- they are responsible for the proper functioning of all the databases of an enterprise and grant or revoke its services to the employees of the company depending on their requirements. They are also responsible for database backups and recoveries.


The essential skills and talents of a database administrator include database backup and recovery, data security, data modeling, and design, etc.

4. Machine Learning Engineer:-

Machine learning engineers are in high demand today. However, the job profile comes with its challenges. Apart from having in-depth knowledge in some of the most powerful technologies such as SQL, REST APIs, etc. machine learning engineers are also expected to perform A/B testing, build data pipelines, and implement common machine learning algorithms such as classification, clustering, etc.


Firstly, you must have a sound knowledge of some of the technologies like Java, Python, JS, etc. Secondly, you should have a strong grasp of statistics and mathematics.

5. Jr. Data Scientist:-

They mostly involve being able to work in a team, possessing a passion for data science and data analysis, creating specific systems, and tracking how they perform over time, data mining, and so on.


The mandatory skills:




“Other” skills:

Level of education (MS & Ph.D. are highly preferred)

Professional development (workshops, MOOCs, certifications, etc.)

Project portfolio (proof of analytics applications)

Work experience (proof of the 3 mandatory skills in the industry setting)

Results (proof of finishing a project in your project portfolio or work experience)

6. Data Scientist:-

Data scientists have to understand the challenges of business and offer the best solutions using data analysis and data processing. For instance, they are expected to perform predictive analysis and run a fine-toothed comb through “unstructured/disorganized” data to offer actionable insights. They can also do this by identifying trends and patterns that can help the companies in making better decisions.


You have to be an expert in R, MatLab, SQL, Python, and other complementary technologies. The main skills needed for a data scientist job:

Coding — R/Python, SQL, Excel, Tableau

Statistics — Basic stats and math like mean, median, averages, statistical differences, chi-square tests, etc.

Domain Knowledge

7. Data Architect:-

A data architect creates the blueprints for data management so that the databases can be easily integrated, centralized, and protected with the best security measures. They also ensure that the data engineers have the best tools and systems to work with.


Data architecture requires expertise in data warehousing, data modeling, extraction transformation, and load (ETL), etc.

8. Statistician:-

A statistician, as the name suggests, has a sound understanding of statistical theories and data organization. Not only do they extract and offer valuable insights from the data clusters, but they also help create new methodologies for the engineers to apply.


A statistician has to have a passion for logic. They are also good with a variety of database systems such as SQL, data mining, and various machine learning technologies.

9. Business Analyst:-

They do have a good understanding of how data-oriented technologies work and how to handle large volumes of data, they also separate the high-value data from the low-value data. In other words, they identify how Big Data can be linked to actionable business insights for business growth.


They should have an understanding of business finances and business intelligence, and also the IT technologies like data modeling, data visualization tools, etc.

10. Data and Analytics Manager:-

A data and analytics manager oversees the data science operations and assigns the duties to their team according to skills and expertise. Their strengths should include technologies like SAS, R, SQL, etc. and of course management.


They must have excellent social skills, leadership qualities, and an out-of-box thinking attitude. You should also be good at data science technologies like Python, SAS, R, Java, etc.

11. Data Science Manager:-

Data science managers need to be good managers in general. A good manager has a vision, is goal-oriented, cares for the team, listens to them for making decisions, is a mentor and coach, empowers and inspires team members, and avoids micromanagement.


They must have excellent social skills, leadership qualities, and an out-of-box thinking attitude. Data Science Manager possesses all the elements to make informed decisions about building a data-centric product.

12. Data Science Consultant:-

A successful data science consultant requires a wide range of skills, including domain knowledge, business acumen, analytical thinking & problem-solving, teamwork & project management, communication & presentation, machine learning, big data, and software development.


Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naïve Bayes, SVM, Decision Forests, etc. Experience with common data science toolkits, such as R, Weka, NumPy, MATLAB, etc.

Proficiency in using query languages such as SQL, Hive, Pig

Great communication skills

Experience with NoSQL databases, such as MongoDB, Cassandra, HBase

Good applied statistics skills, such as distributions, statistical testing, regression, etc.

Data-oriented personality

13. AI Consultant:-

They have the ability to design, build, and deploy predictive and prescriptive models using statistical modeling, machine learning, and optimization. Ability to use structured decision-making to complete projects. Ability to manage an entire ML project from business issue identification, data audit to model maintenance in production.


Ability to solve complex business challenges

Ability to design, build and deploy predictive and prescriptive models

Ability to use structured decision-making to complete projects

Ability to manage an entire ML project from the business issues and many more.



shashank shukla

I have Leverage communication and leadership skills to interact with customers and teams.