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Reading list

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
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 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

Top 13 Companies Hiring Data Science Freshers in 2024

Pankaj Singh
Pankaj Singh 01 Aug, 2024
9 min read

Introduction

If you are in your final year of engineering or have completed your data science course, your eagerness to join MNCs is understandable. You’ve probably binged on countless YouTube videos about the best-paying data science companies but still haven’t found the right fit. Don’t worry, you’re not alone!

We’re excited to bring you the latest scoop on the best companies to kickstart your career in Data Science. These organizations are not just stepping stones but launch pads for your professional journey. Imagine working with these giants and contributing to projects that make a real difference! So, without further ado, let’s dive into the top companies for Data Science freshers in 2024.

Overview

  • The article highlights the top companies hiring data science freshers in 2024, including Amazon, Google, Microsoft, and startups like MathWorks and Arya.AI.
  • It provides a refresher for data science freshers, covering core concepts, programming skills, machine learning, data preprocessing, model evaluation, and SQL basics.
  • Tips for increasing selection chances include building a strong resume, gaining practical experience, networking, preparing for interviews, staying updated with industry trends, and considering advanced education.

Table of contents

Refresher for Data Science Freshers

Here’s a focused refresher for Data Science freshers preparing for interviews:

  1. Core concepts
    • Statistical measures (mean, median, mode, variance)
    • Probability basics
    • Hypothesis testing and p-values
    • Correlation vs. causation
  2. Programming
    • Python fundamentals (data structures, functions, OOP)
    • Key libraries: pandas, numpy, scikit-learn
    • Basic data manipulation and cleaning
  3. Machine Learning
    • Supervised vs. unsupervised learning
    • Common algorithms: linear/logistic regression, decision trees, k-means
    • Overfitting and underfitting
    • Train-test splits and cross-validation
  4. Data preprocessing
    • Handling missing data
    • Feature scaling methods
    • One-hot encoding vs. label encoding
  5. Model evaluation
    • Metrics: accuracy, precision, recall, F1-score, ROC-AUC
    • Confusion matrix interpretation
  6. SQL basics
    • SELECT, WHERE, GROUP BY, JOIN operations
  7. Common interview questions:
    • Explaining your past projects
    • Describing your workflow for a data science problem
    • Ethical considerations in data science
  8. Practical tips:
    • Review your resume and be ready to discuss all points
    • Prepare questions about the company and role

For detailed information, here are the links for interview questions and learning path:

  1. Top 100 Data Science Interview Questions & Answers 2024
  2. Learning Path to Become a Data Scientist in 2024

Top Companies for Data Science Freshers

Here are the top companies hiring for Data Scientists:

1. Amazon

Amazon

Amazon is a leader in e-commerce and cloud computing, constantly seeking data scientists to improve its recommendation systems, logistics, and AWS services. They offer diverse roles across machine learning, AI, and big data analytics. Amazon’s data science positions often require Python, SQL, and cloud technologies expertise. They provide competitive salaries and opportunities for career growth.

Job Link: Amazon Data Science Job

Expected Salary: INR 15,00,000 – 25,00,000 || USD 20,000 – 33,000

2. Google (Alphabet)

Google

Google, under Alphabet, is at the forefront of AI and machine learning research. They hire data scientists for projects like search algorithms, Google Cloud, and self-driving cars. Positions often require advanced degrees and strong coding skills. Google offers a collaborative work environment, cutting-edge projects, and excellent benefits, making it a top choice for data scientists.

Job Link: Google Data Science Job

Expected Salary: INR 18,00,000 – 30,00,000 || USD 24,000 – 40,000

3. Microsoft

Microsoft

Microsoft hires data scientists across various divisions, including Azure, Office 365, and Xbox. They focus on AI, cloud computing, and business intelligence. Microsoft offers opportunities to work on large-scale data problems and develop innovative solutions. They value candidates with strong analytical skills, programming expertise, and knowledge of machine learning algorithms. Microsoft provides competitive compensation and a global work environment.

Job Link: Microsoft Data Science Job

Expected Salary: INR 15,00,000 – 25,00,000 || USD 20,000 – 33,000

4. EY

EY

Ernst & Young (EY) hires data scientists to support their consulting and auditing services. They focus on leveraging data analytics to provide insights for clients across various industries. EY offers opportunities to work on diverse projects, from financial modeling to risk assessment. They value candidates with strong statistical skills, business acumen, and the ability to communicate complex findings to non-technical stakeholders.

Job Link: EY Data Science Job

Expected Salary: INR 6,00,000 – 12,00,000 || SD 8,000 – 16,000

5. JPMorgan Chase & Co.

JP Morgan Chase & Co.

JPMorgan Chase hires data scientists to enhance its financial services, risk management, and fraud detection systems. The company offers roles in quantitative research, machine learning, and predictive modeling. Candidates with strong mathematical backgrounds and programming skills are valued. JPMorgan Chase provides opportunities to work with large-scale financial data and develop innovative solutions for the banking industry.

Job Link: JPMorgan Chase & Co. Data Science Job

Expected Salary: INR 8,00,000 – 15,00,000 || USD 10,700 – 20,000

6. PwC

pwc

PricewaterhouseCoopers (PwC) hires data scientists to support its consulting and auditing services across various industries. PwC leverages data analytics for business insights, risk assessment, and process optimization. It values candidates with strong analytical skills, industry knowledge, and the ability to translate complex data into actionable recommendations. PwC offers diverse projects and opportunities for professional growth.

Job Link: PwC Data Science Job

Expected Salary: INR 6,00,000 – 12,00,000 || USD 8,000 – 16,000

7. Databricks

Databricks

Databricks, a leader in unified analytics and machine learning, hires data scientists to work on its cutting-edge platform. They offer roles in developing and implementing scalable data solutions, machine learning models, and big data analytics. Databricks value expertise in distributed computing, Python, and Spark. They provide opportunities to work with leading-edge technologies and contribute to open-source projects.

Job Link: Databricks Data Science Job

Expected Salary: INR 15,00,000 – 25,00,000 || USD 20,000 – 33,000

8. IBM

IBM

IBM hires data scientists for various projects, including Watson AI, cloud computing, and business intelligence solutions. They offer roles in machine learning, natural language processing, and cognitive computing. IBM values candidates with strong analytical skills and experience in big data technologies. They provide opportunities to work on groundbreaking projects and collaborate with global teams.

Job Link: IBM Data Science Job

Expected Salary: INR 7,00,000 – 15,00,000 || USD 9,300 – 20,000

Startups for Data Science

Now, let’s cover the startups:

9. MathWorks

MathWorks

MathWorks, known for MATLAB and Simulink, hires data scientists to develop and improve its technical computing software. It offers roles in algorithm development, signal processing, and machine learning. MathWorks values candidates with strong mathematical backgrounds and programming skills. It provides opportunities to work on cutting-edge scientific computing solutions and collaborate with a global community of researchers.

Job Link: MathWorks Data Science Job

Expected Salary: INR 7,00,000 – 15,00,000

10. Arya.AI

Arya.ai

Arya.AI is an AI startup focusing on deep learning solutions for various industries. It hires data scientists to develop and implement AI models for tasks like computer vision and natural language processing. Arya.AI values candidates with expertise in deep learning frameworks and strong problem-solving skills. It offers opportunities to work on innovative AI projects and contribute to the evolving field of artificial intelligence.

Job Link: Arya.AI Data Science Job

Expected Salary: INR 6,00,000 – 10,00,000

11. Rephrase.ai

Rephrase.ai

Rephrase.ai focuses on AI-powered video synthesis technology. It hires data scientists to work on computer vision, speech synthesis, and facial animation models. Rephrase.ai values candidates with expertise in deep learning, particularly in video and audio processing areas. It offers opportunities to work on cutting-edge video generation technology and contribute to the future of digital content creation.

Job Link: Rephrase.ai Data Science Job

Expected Salary: You can check on their website

12. Yellow.ai

Yellow.ai

Yellow.ai specializes in conversational AI and chatbot solutions. They hire data scientists to develop and improve natural language processing models, intent recognition systems, and dialogue management algorithms. Yellow.ai values expertise in NLP, machine learning, and conversational design. They offer opportunities to work on AI-powered customer engagement solutions and contribute to the evolution of human-computer interaction.

Job Link: Yellow.ai Data Science Job

Expected Salary: You can check on their website

13. NIRMAI

NIRAMAI

NIRMAI (Non-Invasive Risk Assessment with Machine Intelligence) is a healthcare AI startup that hires data scientists to develop AI models for early detection of breast cancer and other medical conditions. NIRMAI values expertise in machine learning, particularly in medical image analysis and predictive modeling. It offers opportunities to work on life-saving AI applications and contribute to advancements in healthcare technology.

Job Link: NIRMAI Data Science Job Link

Expected Salary: You can check on their website

For more information on top startups for Data Science – Top 20 Data Science Companies You Should Know.

Tips to Increase the chances of Selections in Companies for Data Science Freshers

To maximize your chances of securing a position in Companies for Data Science Freshers, focus on the following steps:

  1. Build a Strong Resume: Highlight your technical skills, relevant projects, internships, and certifications. Tailor your resume for each application to emphasize the skills and experiences most relevant to the job description.
  2. Gain Practical Experience: Work on real-world projects, either through internships, freelance work, or personal projects. This experience will enhance your skills and demonstrate your ability to apply theoretical knowledge to practical problems.
  3. Network Actively: Attend industry conferences, workshops, and meetups. Join professional groups on LinkedIn and engage with professionals in your field. Networking can lead to valuable connections and job referrals.
  4. Prepare for Interviews: Study common interview questions and practice coding problems on platforms like LeetCode, HackerRank, or CodeSignal. Be ready to discuss your past projects and experiences in detail.
  5. Stay Updated with Industry Trends: Follow industry news, read relevant blogs, and participate in webinars to stay informed about the latest developments in your field. This knowledge can set you apart during interviews and help you understand the evolving needs of the industry.
  6. Consider Advanced Education: Depending on your career goals, pursuing higher education or specialized certifications can give you a competitive edge and open up more opportunities in your chosen field.

Following these steps can enhance your profile and increase your chances of landing a position at a leading multinational corporation.

Conclusion

In 2024, there are many companies for Data Science Freshers looking to start their careers. Firms like Amazon, Google, Microsoft, EY, JPMorgan Chase, PwC, Databricks, and IBM are actively seeking data scientists to work on cutting-edge projects and technologies. Additionally, startups such as MathWorks, Arya.AI, Rephrase.ai, Yellow.ai, and NIRMAI provide exciting roles in various innovative fields. To maximize your chances of securing a position, focus on building a strong resume, gaining practical experience, networking actively, preparing thoroughly for interviews, staying updated with industry trends, and considering advanced education.

Join the Analytics Vidhya community for an immersive experience! Explore Data Science and Generative AI groups tailored to your interests, learn with peers, and enjoy free live webinars and AMA sessions with industry experts.

Frequently Asked Questions

Q1. Which companies are the top employers for Data Science freshers in 2024?

Ans. Companies like Amazon, Google, Microsoft, EY, JPMorgan Chase, PwC, Databricks, and IBM are top employers for Data Science freshers in 2024.

Q2. What skills are these companies looking for in Data Science freshers?

Ans. Key skills include proficiency in Python, SQL, machine learning algorithms, data preprocessing, statistical analysis, and familiarity with tools like pandas, numpy, and scikit-learn.

Q3. What are the expected salary ranges for Data Science freshers at these companies?

Ans. Expected salary ranges for Data Science freshers can vary, with salaries generally ranging from INR 6,00,000 to 30,00,000 or USD 8,000 to 40,000, depending on the company and location.

Q4. Do these companies require advanced degrees for Data Science positions?

Ans. While some companies like Google may prefer advanced degrees, many value practical experience and demonstrated skills through projects, internships, and relevant coursework.

Q5. How can Data Science freshers improve their chances of being hired by top companies?

Ans. Freshers can improve their chances by building a strong resume, gaining practical experience through internships and projects, networking actively, staying updated with industry trends, and preparing thoroughly for interviews.

companies in data sciencedata science
Pankaj Singh
Pankaj Singh 01 Aug, 2024

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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