Top 6 Data Scientist Jobs



Data scientists are required to systematically mine, analyze, clean, and organize data for their employers. They work with structured and unstructured data in order to make this sourced or generated data usable for the organizations they work for. These tasks are important and generally necessary for all data scientists. However, there are many different responsibilities one can be given as there are many different types of tasks backed by data science. These various job roles require these data scientists to serve different functions. Data scientists can be in charge of modeling solutions or they can also be in charge of analyzing existing processes. Some data scientists are tasked with building data systems or managing databases while others use analytical software to help businesses make data-backed decisions.

Professionals in the data science field are either working with data-based solutions or building data-based systems. Starting from automation projects with machine learning (ML) to predictive analytics, data scientists are necessary for a huge variety of tasks. Without data scientists, organizations would not even be able to make data cognizable. This is why there is a range of data scientist jobs one can choose from. Let us check out the main jobs that budding data scientists take up.

Top 6 Jobs for Data Scientists

Here are the different jobs available for budding data scientists:

Machine Learning Scientist/Engineer

Machine learning scientists deliver automation solutions and facilitate the creation of data tunnels for machine learning systems. These types of data scientists are tasked with designing, building, monitoring and running tests on machine learning systems. Using supervised and unsupervised learning techniques, they identify and then incorporate the best algorithms for predictive analytics or forecasting.


Statisticians work with applied statistics and theoretical concepts to help companies achieve their business goals. These types of data scientists work with software such as SAS and programming languages like R. They use statistical approaches for analytics, data visualisation and real-world applications. Statisticians like mathematicians, can also get involved with actuarial science. A statistician can help build statistical and mathematical models for financial engineering or economics.


Mathematicians are slowly becoming a must for companies as well. This is due to the immense advantage mathematicians bring in during operational research and inventory management. Applied mathematics can help in the optimisation of processes, supply chains and pricing. They are also tasked with identifying the best algorithms that can be used for comparative analytics and determining the probabilities of events or anomalies. Mathematicians are also necessary for actuarial science and banks and other financial institutions are always in need of determining outcomes or predicting markets.

Data Analyst

Data analysts are tasked with the analysis and interpretation of data using analytics methods. They collect, analyse and gain insight from data. They can identify dependencies, trends, anomalies and relationships from unstructured data after transforming datasets. They are also sometimes tasked with cleaning the noise from data, data warehousing and data mining. Analysts are valuable to companies for building reports, visualising and forecasting. One can become a quality analyst or software programming analyst as well. A quality analyst uses data to optimise or improve manufacturing processes or services. Meanwhile, a software programming analyst helps speed up commuting time, development pipelines and data transfer. They also help incorporate better technologies and evaluate or test programs.

Data Engineer/Architect

Data engineers are tasked with facilitating the effective use of data. They are sometimes tasked with modelling data, but they mostly are responsible for processing data. They are also experts in data warehousing and data storing. They batch-process data in real-time to sustain data pipelines and help in creating the most efficient data ecosystem for their organisations. They ensure the safe and easy transfer of data and keep the data network functioning without any problems. Data architects have the same responsibility of maintaining the data ecosystem and ensuring data pipeline requirements are met. However, they are more involved with designing how the data will be used by the systems. These architects design data systems and plan how data will be stored and accessed. They are also tasked with integrating new systems and technologies into pre-existing systems. Data architects must also be experts of cloud computing and able to integrate cloud based-solutions into the data infrastructure of organisations.

Business Intelligence Analyst/Developer

Business intelligence developers design business tools while business intelligence analysts use these tools to help businesses make data-driven decisions. In order to make good strategies, gaining insights from historic and operational data is required. Business intelligence professionals design business intelligence software in order to help companies use this data. These professionals are tasked with customising end-user dashboards, sourcing data, connecting the data and creating visualisations. They help companies migrate to more advanced business intelligence tools from pre-existing systems. Analysts in this field use this kind of software to build reports and share them with management.

Skills Required for Data Science Jobs

Here are some of the necessary skills one needs to join any of the above mentioned jobs:

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  • Python, R or Scala
  • DBMS and Standard Query Language
  • RDBMS such as Microsoft SQL Server, MySQL and MariaDB
  • Business intelligence tools such as Power BI or Tableau
  • Statistical tools such as SAS
  • Microsoft Excel and Power Pivot
  • Cloud computing, data engineering, and data architecture
  • Testing and debugging skills
  • Data processing
  • Data warehousing and data storing
  • Data transforming
  • Data mining and data cleaning
  • Data modeling
  • Mathematical and statistical concepts
  • Logical and algorithmic skills


What should I search on Glassdoor to find Data Scientist jobs in Noida?

There are many types of data scientist jobs near Noida, Uttar Pradesh. You can just search ‘Data Scientist’ on Glassdoor’s search bar with the location set as Noida. You will get the different data scientist jobs that are available and the average data scientist salary in those jobs. If you scroll a bit, then you will also find other related jobs near the city of your choice.

What are the necessary data scientist qualifications?

An undergraduate degree is definitely necessary. However, it can be from a non-technical field. However, belonging from a mathematics, statistics or computing background can definitely prove to be useful. One might not even need to learn additional skills other than the compulsory data science tools and technologies.

Are there jobs in data science for freshers?

Yes, there are many data science jobs for freshers. MNCs hire dozens of data scientists with zero to less than one year of experience.

What are the different data science fields?

The various data science fields are machine learning, artificial intelligence, big data, data mining, business intelligence, software development, quality analytics, and data engineering. There are many more associated fields and subfields.

How does one progress in a data science career?

The data scientist job title hierarchy starts with a junior or assistant data scientist to a data scientist and then finally a senior data scientist. After this, with massive experience, one can become a principal data scientist. Beyond this, one can become the director or vice president of data science.

Is data science fun?

Yes, data science is a very innovative and fun field. The job roles are engaging and require team collaboration.

Key Takeaway

Data science is a massive field with many sub-domains. One can choose among a huge variety of job roles and specialisations. This is what makes data science fun and welcoming to professionals from all kinds of backgrounds and with different interests.