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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Checkout Data Science Salary in India [2024 Updates]

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 05 Feb, 2024
13 min read

Data Science jobs are currently some of the most paying in India, with an average annual salary range of ₹ 4 Lakhs to ₹ 25 Lakhs. Data Science salary in India may vary depending on your skill set, years of experience, job profile, location, and the company. Advantageously, Data Science is a fast-booming multidisciplinary domain that welcomes anybody interested in the topic and willing to learn. You can quickly improve your chances of getting a job in Data Science or increase your existing pay scale by upskilling and keeping yourself updated on the latest trends in the industry. With high-paying data science salaries and online courses to get you to par, a career in Data Science is definitely one to look out for. This article will describe the highest salaries, top skills required, and the job trajectory for Data Scientist salary in India in 2024.

Table of contents

Increasing graph of median salary of Data Scientists from 2015 - 2022
Source: www.analyticsindiamag.com

Top 20 Average Salaries in Data Science Paid by Top Companies in India

1. Apple

Apple is the largest technology company in the world. They manufacture personal devices, develop operating systems, create computer software, and run a video streaming platform. Their catalog includes personal and tablet computers, smartphones, smart speakers, and auxiliary devices. Apple hires Data Scientists for their System Quality team.

  • Salary Range: ₹ 10 LPA – ₹ 75 LPA
  • Experience Level: 2 – 9 years
  • Basic Qualifications: Bachelor’s or Master’s degree in Science or equivalent experience with working knowledge of Data Science, Data Analysis, Data Wrangling, and database management systems such as Cassandra, Spark, or Airflow.

(Source: https://www.ambitionbox.com/salaries/apple-salaries/data-scientist)

2. Google

Google is a multinational tech giant best known for its search engine technology. The company’s portfolio includes computer software, cloud computing, quantum computing, and artificial intelligence. In recent years, Google has expanded to online advertising, e-commerce, e-learning, and consumer electronics, further increasing the need for Data Scientists in these fields.

  • Salary Range: ₹ 10 LPA – ₹ 70 LPA
  • Experience Level: 4 – 9 years
  • Basic Qualifications: Bachelor’s degree in Computer Science or equivalent practical experience with working knowledge of Data Science, Data Analysis, HTML, CSS, HTTP, and JavaScript.

(Source: https://www.ambitionbox.com/salaries/google-salaries/data-scientist)

3. Microsoft Corporation

Microsoft Corporation specializes in inventing technological products and services such as operating systems, computer software, cloud computing, personal tech devices, gaming systems, and more. They also provides online courses and tech support in Data Science and related fields.

  • Salary Range: ₹ 14 LPA – ₹ 60 LPA
  • Experience Level: 1 – 9 years
  • Basic Qualifications: Master’s degree in Computer Science, Statistics, Applied Mathematics, Operations Research, or a relative field, with a keen knowledge of Machine Learning, Data Science, and SQL.

(Source: https://www.ambitionbox.com/salaries/microsoft-corporation-salaries/data-scientist)

4. Amazon Web Services (AWS)

Amazon Web Services (AWS) is an on-demand cloud computing platform developed by the e-commerce giant Amazon. It provides clients with cloud computing services through platforms and APIs on a pay-as-you-go basis. AWS is so widely used today that it has become a core employable skill in Data Science.

  • Salary Range: ₹ 8 LPA – ₹ 60 LPA
  • Experience Level: 3 – 12 years
  • Basic Qualifications: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field with industry experience in Data Science, Big Data, Analytics, Business Intelligence, SQL, Python, and R.

(Source: https://www.ambitionbox.com/salaries/amazon-web-services-salaries/data-scientist)

5. Adobe

Adobe is a computer software company that develops programs focused on digital visualization, e-commerce, documentation, business development, and analytics. The Adobe Creative Cloud is one of the world’s most widely used sets of digital multimedia tools. The company mostly hires Data Scientists for their Enterprise Digital Media Business Intelligence and Research teams.

  • Salary Range: ₹ 11 LPA – ₹ 45 LPA
  • Experience Level: 3 – 9 years
  • Basic Qualifications: A degree in Computer Science, Information Technology, Statistics, or a related discipline, along with work experience in Advanced Data Analytics, SQL, Microsoft Power BI, Microsoft Power Apps, and Microsoft Excel.

(Source: https://www.ambitionbox.com/salaries/adobe-salaries/data-scientist)

6. Oracle

Oracle is one of the largest software companies in the world. It is a database management system that offers a complete suite of integrated cloud applications and cloud infrastructure platforms for clients around the globe. Apart from the Oracle Database, they also develop in-memory, NoSQL, and MySQL databases, helping companies build, run, manage, and migrate their workloads.

  • Salary Range: ₹ 6 LPA – ₹ 37 LPA
  • Experience Level: 2 – 10 years
  • Basic Qualifications: Master’s degree or an advanced qualification in Computer Science, Engineering, Statistics, or Applied Mathematics with an emphasis on Machine Learning and experience in Python and SQL.

(Source: https://www.ambitionbox.com/salaries/oracle-salaries/data-scientist)

7. Bharti Airtel

Bharti Airtel is an Indian multinational telecommunication services company that mainly provides 3G, 4G, and 5G wireless internet services across 18 countries in South Asia and Africa. The company has also expanded into mobile commerce, television broadcasting networks, and digital payment platforms in recent years.

  • Salary Range: ₹ 7.4 LPA – ₹ 36.5 LPA
  • Experience Level: 2 – 8 years
  • Basic Qualifications: Experience and expertise in microservice architectures, relational and NoSQL databases, Unix/Linux, Python, Go/Scala/Java/C/C++/Perl, ML algorithms, OpenCV, Neural networks, and frameworks such as PyTorch, Chainer, and Tensorflow.

(Source: https://www.ambitionbox.com/salaries/bharti-airtel-salaries/data-scientist)

8. Wells Fargo India

Wells Fargo & Company is an American national bank. Internationally, it is a financial services company that offers bank accounts, loans, mortgages, investing, credit cards & banking services through managerial offices around the world. The company’s Indian office hires Data Scientists to handle risk, product modeling, analytics, and automation.

  • Salary Range: ₹ 6.1 LPA – ₹ 35 LPA
  • Experience Level: 2 – 10 years
  • Basic Qualifications: Bachelor’s degree with experience in Python, SQL, Hive, Data Mapping, and Data Modeling, and product knowledge in the finance or banking sector.

(Source: https://www.ambitionbox.com/salaries/wells-fargo-salaries/data-scientist)

9. Samsung

Samsung, headquartered in South Korea, is one of the world’s largest manufacturers of electronic devices. Their catalog features various consumer and industry electronics, including home appliances, digital media devices, semiconductors, memory chips, computer peripherals, and integrated systems. Data Scientists at Samsung help drive technological decisions based on data analytics and statistics.

  • Salary Range: ₹ 7 LPA – ₹ 34 LPA
  • Experience Level: 2 – 12 years
  • Basic Qualifications: Bachelor’s degree, Master’s degree, or Ph.D. in Computer Science, Electronics, Information science, or Statistics from premier institutes, with industry experience in algorithms, data structures, performance optimization, and HLD and LLD architecture.

(Source: https://www.ambitionbox.com/salaries/samsung-salaries/data-scientist)

10. VMware Software

VMware is a cloud computing company focused on virtualization technology. The primary software developed by the company is a desktop virtualization platform that helps clients run multiple operating systems on a single server. It has now expanded to provide multi-cloud services for apps.

  • Salary Range: ₹ 10 LPA – ₹ 32.1 LPA
  • Experience Level: 3 – 11 years
  • Basic Qualifications: Master’s degree or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, Statistics, Applied Mathematics, or a related field with a working knowledge of ML, AI, Data Mining, Data Analytics, Jupyter, pandas, NumPy, sklearn, Keras, Spark, AWS Sagemaker, Lambda, and API Gateway.

(Source: https://www.ambitionbox.com/salaries/vmware-software-salaries/data-scientist)

11. Reliance Jio

Reliance Jio Infocomm Limited operates a national LTE network, an OTT platform, an online payment platform, and various apps. Data Scientists at Jio mainly work in Backend Development with Big Data / DWH / ETL. The company also hires Data Scientists as instructors in the Department of Artificial Intelligence and Data Science at the Jio Institute.

  • Salary Range: ₹ 6 LPA – ₹ 32 LPA
  • Experience Level: 1 – 7 years
  • Basic Qualifications: Bachelor’s degree in Engineering with a keen knowledge and experience in, Data Science, Data Analytics, Neural Networks, Python/PySpark, H2O, Mahout, PyTorch/TensorFlow, ML Ops Platform, and advanced ML techniques like Random Forest, Support Vector Machine, Gradient Boosting Machine, XGBoost etc.

(Source: https://www.ambitionbox.com/salaries/reliance-jio-salaries/data-scientist)

12. Cisco Systems

Cisco is a multinational digital communications technology conglomerate that works on simplifying networking operations. They offer products and services such as workload-optimizing software, wireless devices, security solutions, and cloud-based infrastructure to several major companies worldwide.

  • Salary Range: ₹ 9 LPA – ₹ 29 LPA
  • Experience Level: 2 – 10 years
  • Basic Qualifications: Master’s degree or Ph.D. in Statistics, Operation Research, Applied Mathematics, Economics, Computer Science, or any related discipline, with proficiency in using Microsoft Excel, SQL, Statistical Modelling, Web Analytics, Python, and R.

(Source: https://www.ambitionbox.com/salaries/cisco-systems-salaries/data-scientist)

13. Hewlett-Packard (HP)

Hewlett-Packard, famously known as HP, is one of the world’s biggest manufacturers of desktops, personal computers, digital accessories, printers, and related supplies. The company has also gotten into providing 3D printing solutions in recent years.

  • Salary Range: ₹ 7.1 LPA – ₹ 29 LPA
  • Experience Level: 2 – 11 years
  • Basic Qualifications: Master’s degree or higher education in Computing, Statistics, Mathematics, or Economics with a deep knowledge of Probability and Algorithms and practical experience in ML platforms (like Databricks), Big Data (like Spark or Scala), R, and Python.

(Source: https://www.ambitionbox.com/salaries/hewlett-packard-salaries/data-scientist)

14. Ericsson

Telefonaktiebolaget LM Ericsson is a Swedish telecommunications and networking company operating in various countries across the globe. The company helps to develop, set up, and manage networks, cloud software services, IoT platforms, and 5G infrastructure for emerging businesses.

  • Salary Range: ₹ 6 LPA – ₹ 29 LPA
  • Experience Level: 2 – 13 years
  • Basic Qualifications: Concrete knowledge of Python, Jupyter Hub, Tensorflow, Keras, sci-kit-learn, MapReduce, PySpark, Deep Autoencoders, Neutral Networks, Quantum Computing, CNN, RNN, Bayesian, and other ML and AI applications.

(Source: https://www.ambitionbox.com/salaries/ericsson-salaries/data-scientist)

15. IBM

International Business Machines Corporation (IBM) is known to have played an essential role in the digital transformation of a vast majority of businesses worldwide. The company develops hardware and software products such as computers, servers, storage systems, and networking equipment.

  • Salary Range: ₹ 4.7 LPA – ₹ 27.5 LPA
  • Experience Level: 1 – 11 years
  • Basic Qualifications: Bachelor’s degree and a working knowledge of Unix Commands, Unix Shell Scripting, Data Ingestion, Data Cleansing, ETL Datastage, and Dara Mart Creation.

(Source: https://www.ambitionbox.com/salaries/ibm-salaries/data-scientist)

16. SAP

SAP is one of the world’s leading companies developing enterprise software to manage business operations and customer relations. They provide Business Intelligence tools across cloud, analytics, mobile, and IT domains for all kinds of industries. The German tech firm has 5 offices across India.

  • Salary Range: ₹ 10.5 LPA – ₹ 27 LPA
  • Experience Level: 2 – 9 years
  • Basic Qualifications: A degree in Engineering or an MCA from a tier-1 institution, with experience in JAVA/Python/Shell, Google Cloud Platform/Microsoft Azure/Amazon Web Services, Groovy, Maven, Jenkins, and SCRUM, and a working knowledge of SAP Basis.

(Source: https://www.ambitionbox.com/salaries/sap-salaries/data-scientist)

17. Dell

Dell is a leading computer manufacturing company that makes laptops, desktops, gaming systems, computer accessories, workstations, and servers. They hire Data Scientists as consultants, advisors, and leads across various departments.

  • Salary Range: ₹ 8 LPA – ₹ 27 LPA
  • Experience Level: 2 – 12 years
  • Basic Qualifications: Bachelor’s degree in a STEM-related field and working proficiency in Python, R, VBA, Tableau, Qlik, PowerBI, d3.js, SQL, NoSQL, Oracle, data extraction, transformation, statistics, machine learning, NLP, optimization, simulation, and other related techniques. Bachelor’s degree in Engineering or a related field with hands on experience in Python, T-SQL, PostgreSQL, PL/SQL, MPP data warehousing, and ETL Tools such as StreamSets, Informatica, and Alteryx.

(Source: https://www.ambitionbox.com/salaries/dell-salaries/data-scientist)

18. Deloitte

Deloitte, one of the world’s leading corporate auditing and tax consulting firms, hires Data Scientists and Data Analysts to their Strategy & Analytics wing. Most of the work in this domain deals with digital risk management, providing AI & cloud services for companies, and developing ERP & SaaS solutions for businesses.

  • Salary Range: ₹ 7.5 LPA – ₹ 26 LPA
  • Experience Level: 2 – 7 years
  • Basic Qualifications: Bachelor’s degree in a STEM-related field and working proficiency in Python, R, VBA, Tableau, Qlik, PowerBI, d3.js, SQL, NoSQL, Oracle, data extraction, transformation, statistics, machine learning, NLP, optimization, simulation, and other related techniques.

(Source: https://www.ambitionbox.com/salaries/deloitte-salaries/data-scientist)

19. Fractal Analytics

Fractal Analytics is an artificial intelligence company that develops commercial and purpose-built AI-powered software for companies. The firm’s portfolio includes companies dealing in consumer packaged goods, insurance, healthcare, life sciences, retail and technology, and financial services.

  • Salary Range: ₹ 9 LPA – ₹ 25 LPA
  • Experience Level: 2 – 6 years
  • Basic Qualifications: Bachelor’s degree or equivalent experience in Computer Science, Statistics, Engineering, Informatics, Data Science, or a related field with practical knowledge in Artificial Intelligence, Data Science, and Python.

(Source: https://www.ambitionbox.com/salaries/google-salaries/data-scientist)

20. EXL Service

EXL Service is a global analytics and digital solutions company that serves businesses to set foot and stand in the digital world. The company handles Data Analytics and AI Operations for various industries such as insurance, healthcare, banking, financial services, media, retail, and more.

  • Salary Range: ₹ 6.6 LPA – ₹ 25 LPA
  • Experience Level: 1 – 8 years
  • Basic Qualifications: Bachelor’s degree in Engineering or Computer Applications, with a deep knowledge of Machine Learning, Excel, Power BI, Python, Java, SQL, Spring, etc., and some experience working for a particular industrial domain.

(Source: https://www.ambitionbox.com/salaries/exl-service-salaries/data-scientist)

Top 5 Technical Skills Required as a Data Scientist

Top skills required as a Data Scientist
Source: www.dqlab.id

The 5 main technical skills required to be a Data Scientist are:

  1. Programming Languages & Software: A Data Scientist must be well versed in various programming languages, such as Python and R, to operate and integrate AI and ML-driven applications for business automation and efficiency. Data Scientists must also know to use suitable packages to create algorithms required for the specific outcome.
  2. Statistics & Applied Mathematics: The job of a Data Scientist involves a lot of decision-making based on data statistics and calculations involving unknown variables. Hence, a Data Scientist needs to be familiar with multivariate calculus, algebraic formulae, mathematical applications, and the various formulae and theories in statistics.
  3. Storytelling with Data: Data Scientists often work with unstructured and raw data, which they must know to organize, read, and efficiently explain to all stakeholders. Knowledge in data extraction, processing, wrangling, exploration, analytics, and visualization helps to clean out raw data and unify them based on patterns. Tools like Power BI, Tableau, QlikView, Google Analytics, Microsoft Excel, Fusion Charts, Plotly, etc., are used for this purpose.
  4. Artificial Intelligence: One of the roles of a Data Scientist is to digitize companies by incorporating different AI-based applications into their businesses. Machine Learning and Deep Learning algorithms help machines think like human minds and predict people’s behavior and decision-making thought processes. Python libraries like PyTorch, TensorFlow, Keras, Scala, etc., are commonly used in creating these algorithms.
  5. Big Data Processing Frameworks: As Data Scientists work with large amounts of data, they must be skilled in processing this data using Big Data Processing Frameworks. Working knowledge of Hadoop, Apache Spark, Apache Storm, Hive, MapReduce, and similar frameworks, as well as cloud computing software like AWS, Azure, and Google Cloud, is thus a necessary skill to have as a Data Scientist.

Salary and Job Trajectory for a Data Scientist in India

Salary comparison of Data Scientists in India based on experience.

Source: www.salaryexplorer.com

The salary of a Data Scientist depends on their qualification, experience, skill set, and location. A successful career in Data Science built on a solid educational background and continuous upskilling guarantees growth in pay and perks. Here’s a basic outline of the job trajectory for a Data Scientist in India.

  • Data Science Intern: Soon after graduation, you can enter the Data Science domain as an intern to gain a few years of related real-world experience and hone the practical skills required for the job. An entry-level Data Science job pays between ₹ 1.8 Lakhs and ₹ 11.3 Lakhs per annum.
  • Junior Data Scientist: A Data Scientist with 1 – 4 years of experience is considered a Junior Data Scientist. They mostly have a Master’s degree and are paid from ₹ 3.46 Lakhs to ₹ 20 Lakhs per annum in India.
  • Data Scientist: As you gain 5 – 9 years of experience in Data Science, your annual salary will increase to something between ₹ 6.06 Lakhs and ₹ 30 Lakhs.
  • Senior Data Scientist: Senior Data Scientists with a broad range of skills and an experience of 10 – 15 years can earn between ₹ 10 Lakhs and ₹ 45 Lakhs in a year in India.
  • Lead Data Scientist: A Lead Data Scientist usually has a Ph.D. in Data Science or a related discipline and boasts an illustrious career spanning over 15 years. They are likely to be paid anywhere from ₹ 24 Lakhs to a whooping ₹ 1 Crore per annum.
Job trajectory and average salaries of Data Scientists in India - 2023

Source: www.mygreatlearning.com

Top 10 FAQs About Data Scientist Salary in India

Q1. How much does a data scientist in India make?

A. A data scientist in India can make ₹ 1.8 – ₹ 11.3 LPA as an entry-level intern. With experience, a Junior Data Scientist can make ₹ 3.46 to ₹ 20 LPA, and a Senior Data Scientist can make ₹ 10 to ₹ 45 LPA in India. A Lead Data Scientist in India earns between ₹ 24 Lakhs and ₹ 1 Crore in a year.

Q2. Is data science a high-paying job?

A. With the expansion of digitalization in the world, data science has grown to be one of the highest-paying jobs in the world. In India, data scientists make between ₹ 1.8 Lakhs to  ₹ 1 Crore a year, depending on their qualifications, skill set, and experience.

Q3. Is data science a promising career?

A. Data science is a great career option for people with an engineering, mathematics, statistics, computer science, or related background. The applications of data science and job opportunities in the domain are increasing every day, making it one of the most sought-after careers in the world.

Q4. How do I increase my salary in Data Science?

A. The salary of a data scientist increases with experience, qualification, and better skills. You can increase your salary in Data Science by upskilling with relevant courses and enhancing your skill set with more role-based experiences.

Q5. What are the top 10 highest-paying data science jobs?

A. The top 10 highest-paying data science jobs include Data Analyst, Data Scientist, Data Engineer, Quantitative Analyst, Data Warehouse Architect, Machine Learning Engineer, Machine Learning Scientist, Statistician, Business Analyst, and Marketing Analyst.

Q6. Which companies pay the highest salaries for Data Scientists in India?

A. Google, Microsoft, AWS, Adobe, and Oracle are among the companies paying the highest salaries for Data Scientists in India.

Q7. Which country pays the highest salary for data scientists?

A. The United States of America pays the highest data scientist salaries on average, followed by Australia, Canada, and Germany.

Q8. Can a data scientist earn 1 crore rupees?

A. Yes, a data scientist with a Ph.D. in Data Science or a related field, a broad skill set, and a diverse work experience of over 15 years, can earn up to 1 crore rupees per annum in India.

Q9. What is the average salary of a data scientist?

A. The average salary of a data scientist in India ranges from ₹ 4 Lakhs to ₹ 25 Lakhs per annum.

Q10. What is a fresher data scientist’s salary in India?

A. The salary of a fresher data scientist in India is in the range of ₹ 1.8 – ₹ 11.3 Lakhs per annum.

Start Your Data Science Journey Today!

Technological advancements in recent years have increased the importance of Data Science across different fields of work. The applications of Data Science have also branched beyond data collection and analysis, making it a multidisciplinary domain with many different roles. With high-paying data science salaries and guaranteed career growth, the number of people entering the Data Science domain is increasing daily. How can you be a part of it? Analytics Vidhya offers a wide range of courses that help you build a career in Data Science. Here you will find job-guaranteed training courses like the Bootcamp Program catered to data science enthusiasts and beginners, as well as comprehensive mentored courses like the BlackBelt Plus Program for employees who wish to be leaders in the industry. Still not sure and want to know more? Check out our free courses here to learn about Data Science and determine what role best fits you.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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