Junior Data Scientist: Important Roles & Responsibilities


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There were times when businesses had limited data sources. It was easy for them to manipulate this data according to their needs. With the evolution of technology, businesses are now generating complex data at an unstoppable pace. It only makes sense to utilize this data and extract meaningful insights from it. This is exactly where Data Science comes in.

Data Science is the process of uncovering findings from data or surfacing the hidden insights that can help businesses make smart and strategic decisions. You can take the example of Netflix where it analyzes your movie viewing pattern to understand what drives your interest and accordingly recommend different movies. This might sound easy but is highly complex. This is why companies have Data Scientists.

Data Scientists are analytical experts who use their multidisciplinary skills to extract trends and meaningful insights from the data. The journey of a Data Scientist starts as a Junior Data Scientist. A Junior Data Scientist should have expertise in 4 fields viz., Mathematics & Statistics, Programming & Databases, Domain Knowledge, and Communication & Interpersonal Skills.

This article will take you through the journey of a Junior Data Scientist. It will help you understand the key roles and responsibilities of a Junior Data Scientist. It will also brief you on the skills required to become a Junior Data Scientist.

Table of Contents

Introduction to a Data Scientist

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The progression of a Data Scientist’s career generally has 5 levels:

  • Level 1 – Data Analyst/Junior Data Scientist: The journey of a Data Scientist starts as a Data Analyst, also known as a Junior Data Scientist if he has Machine Learning skills. He is someone with 0-2 years of experience in this field. 
  • Level 2 – Data Scientist: A Data Scientist is someone who has 3-7 years of experience as a Junior Data Scientist. He builds Predictive Models for the company and trains Junior Data Scientists.
  • Level 3 – Senior Data Scientist: A Senior Data Scientist is someone who has 2-3 years of experience as a Data Scientist. A Senior Data Scientist formulates data problems end to end from a client’s project and accordingly deploys different models to deliver meaningful results.
  • Level 4 – Principal Data Scientist: A Principle Data Scientist is someone who handles a team of Senior Data Scientists and works on multiple projects at a time.
  • Level 5 – Head of Data Science: A Head of Data Science is also known as a Chief Data Scientist. This is the topmost position and is someone who has 15-20 years of industry experience. He is the face of the Data Science department of a company in front of a client. He leads and drives the whole team of Data Scientists of a company. 

There are also job roles like Data Science Consultant, Data Science Manager, Big Data Scientist that fit somewhere in this hierarchy but highly depend on the company’s infrastructure.

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Introduction to a Junior Data Scientist

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A Junior Data Scientist is an entry-level Data Scientist who in some companies is also known as Data Analyst. He is someone who has 0-2 years of experience in the field of Data Science. He could be someone who has just graduated from his college. A Junior Data Scientist’s job mostly revolves around using simple Machine Learning algorithms for daily tasks such as performing analyses on client’s data, extending certain parts of the company’s data, simplifying data problems, testing new algorithms, writing up results to share with others, etc.

As experience is something a Junior Data Scientist lacks, it becomes important for him to have necessary skills like a good understanding of Mathematics & Statistics, Programming & Databases, Domain Knowledge, and Communication & Interpersonal skills. Apart from these skills, a Junior Data Scientist should be highly motivated, passionate, hard-working, and should have thirst for knowledge.

Roles and Responsibilities of a Junior Data Scientist

Most businesses have now realized the importance of data-driven decision-making. Data Science has now become inseparable from day-to-day operations. It is employed in Product Marketing, Engineering, and Sales to make strategic decisions. This is why it becomes crucial for a Junior Data Scientist to understand his roles and responsibilities. Listed below are the roles and responsibilities of a Junior Data Scientist:

  • Ask the right questions to understand the problem.
  • Extract data from multiple data sources.
  • Clean the data and remove all the noise.
  • Integrate and Load Data to a centralized location (if needed).
  • Perform exploratory Data Analysis on the data loaded.
  • Choose the right Predictive or Machine Learning model for the problem.
  • Apply those models and extract meaningful insights.
  • Measure and improve results (if needed).
  • Present final results to a senior and make adjustments based on the feedback.
  • Repeat the process to solve the problem.

Job Titles of Data Scientist

The rising field of Data Science has many different job titles, and not every business using Data Science is specifically looking for Data Scientists. Here are a few of the most sought-after data science job titles.

  • Analytics Manager
  • Associate data scientist
  • Business Intelligence Analyst
  • Data Analyst
  • Data Architect
  • Data Engineer
  • Data Scientist (entry-level)
  • Data Scientist (senior-level)
  • Director/VP/SVP
  • Lead data scientist
  • Product Manager
  • Research Analyst
  • Research Scientist
  • Statistician

Skills Required to Become a Junior Data Scientist

A Junior Data Scientist is responsible for Descriptive and Predictive Analysis. There are 7 skills required to become a successful Junior Data Scientist:

1) Database Knowledge

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A Junior Data Scientist is expected to have good Database Knowledge in order to carry out Data Analysis with the data stored in relational databases. Some of the well-known databases are MySQL, Oracle Database, Microsoft SQL Server, Teradata, etc.

2) Mathematics & Statistics

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Statistics is the science that deals with developing and studying methods for collecting, analyzing, interpreting, and presenting Empirical Data. A Junior Data Scientist needs to have a stronghold on this for Data Analysis.

3) Programming Tools

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Programming tools such as R, Python, and SAS are some of the most popular ones in the field of Data Science. It is not required by a Junior Data Scientist to learn all the programming tools available but to master one. 

  • R Programming: R Programming is a free software environment used for Statistical Computing and Graphics. It supports most Machine Learning algorithms for Data Analytics like Regression, Association, Clustering, etc.
  • Python: Python is one of the most popular open-source general-purpose Programming Languages available in the market. Libraries like NumPy and SciPy are some of the widely used Python libraries in Data Science.
  • SAS: SAS is a programming language that can mine, alter, manage, and retrieve data from a variety of data sources and can perform Statistical Analysis on the data.

4) Data Wrangling

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A Junior Data Scientist needs to learn how to wrangle data. Data Wrangling is the process of cleaning, manipulating, and organizing messy and complex data for easy Data Analysis. It involves 6 iterative steps:

  • Discovering: In this step, a Junior Data Scientist needs to dive deeply and understand the structure of his data. This will give him an idea of what the data is about and how it can be analyzed.
  • Structuring: In this step, data is organized. Raw data is generally given in different shapes and sizes. In most cases, there will be no fixed structure of the data. A Junior Data Scientist is expected to restructure this data and organize it for easier computation and analysis.
  • Cleaning: In this step, the data is cleaned for high-quality analysis. Null Values are changed and the formatting is standardized to make the data free of any noise.
  • Enriching: In this step, a Junior Data Scientist needs to take stock of what is in the data and accordingly strategize on adding more data to augment it.
  • Validating: In this step, a Junior Data Scientist is expected to use Repetitive Programming to verify the Consistency, Quality, and Security of the data.
  • Publishing: In this step, data is finally readied for analytics. The wrangled data is refined and made available for analytical purposes.

5) Machine Learning

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A Junior Data Scientist needs to master the concepts of Machine Learning. Machine Learning provides systems the ability to automatically learn and improve from experience without any human intervention or explicit programming. It can be achieved through various algorithms such as Regression, Naive Bayes, Support Vector Machine (SVM), K Means Clustering, K-Nearest Neighbor (KNN), and Decision Tree algorithms to name a few.

6) Big Data

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This is more of an optional skill for a Junior Data Scientist. Big Data is a term that describes large and complex data that cannot be analyzed using Data Processing tools. Some of the most popular tools used by Big Data Scientists are Tableau, Apache Spark, Talend, Hadoop, etc.

For more information on Big Data, click here.

7) Data Visualization

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A Junior Data Scientist should be capable of Data Visualization. Data Visualization involves Integrating Datasets, Analyzing Models, and Visualizing them in the form of graphs, charts, etc. Some of the widely used Data Visualization tools are Tableau, PowerBI, Google Data Studio, etc.

How to Become a Data Scientist?

If you’re wondering how to become a data scientist, I can assure you that it takes time, work, and education. Such profitable professions don’t just appear out of thin air.

You must set aside time to learn data science, improve your abilities, look for work, and get ready for interviews if you want to pursue employment as data scientists. All the abilities we covered here may be learned very effectively through Simplilearn’s Data Science Certification Courses.

Take into account these as you begin your data scientist profession.

  • Be patient; it may take a few applications before you land a position. Develop your business, programming, and data analytics skills.
  • Be ready to respond to questions in a data science interview regarding your behaviour in various workplace scenarios. Keep in mind that employers are more interested in your problem-solving skills and approach to particular situations than your technical knowledge.
  • Think about obtaining a certificate in Business Analytics or Data Science.
  • Make a portfolio to display some of the projects you’ve completed using actual data.

How Much Can a Data Scientist Earn?

Data scientists typically have better demand and pay than the majority of other ITES positions. One of the most important variables in defining a data science professional’s wage range is experience.

A Data Scientist in the United States makes, on average, USD 117,212 a year, and the same website claims that Data Scientists in India make, on average, INR 1,000,000.

Data Scientist Career Path

Currently, data science is regarded as one of the most profitable professions. Data scientists are needed by businesses in all major sectors and industries to assist them in gaining insightful knowledge from large data. Demand for highly qualified data science specialists who can work in both the business and IT worlds is rising quickly.

As a relatively new profession, becoming a data scientist has no well defined career path. People who have backgrounds in economics, computer science, statistics, mathematics, and other fields eventually work in data science.

Data scientist occupations provide a wide range of alternatives. Data science is an interdisciplinary field, thus there are multiple exposure and departure choices.


This article introduced you to Data Scientists and Junior Data Scientists in particular. It helped you understand the roles and responsibilities of a Junior Data Scientist. It also provided the skills required to become a Junior Data Scientist.

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Karan Singh Pokhariya
Former Research Analyst, Hevo Data

Karan has experience in driving strategic planning, and implementing data-driven initiatives. His experience spans strategic transition planning, data analysis for optimization, product development. His passion to data drives him write in-depth articles on data integration.

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