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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

How to Become Amazon Data Scientist in 2023?

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Analytics Vidhya Last Updated : 04 Jul, 2023
7 min read

Introduction

Most businesses currently generate, compile, and manage enormous volumes of data. Yet, most of them lack the knowledge to fully utilize every bit of the data they have collected. Data scientists come into play in this scenario. Data scientists, an emerging class of analytical data specialists, are an increasingly important player in businesses. Amazon is recognized to be among the best companies for data scientists to pursue careers because of its attractive salary and interesting career prospects. 

Table of contents

What is the Role of a Data Scientist at Amazon?

Amazon data scientist job role
Source: Springboard

The role and responsibilities of an Amazon data scientist are as follows: 

  1. A data scientist’s job at Amazon is to balance business and technological expertise.
  2.  The goal of an Amazon data scientist is to develop and enhance technologies that can help business models operate in an efficient manner that meets the most achievable objectives. 
  3.  Amazon employs data scientists who are cooperative. Each team completes a distinct project. No matter your skills or expertise, you should always realize that you do not stand solo in what you choose to do. 
  4.  Scientists employ statistical and analytical techniques and AI tools to automate some organizational functions and provide clever solutions to business-related challenges. After analyzing the data, they present the findings understandably and interestingly.
  5.  They create algorithms to train Alexa to comprehend individual requests and commands and use AI to help Amazon Web Services users create customized ML models.

Skills Required to Become Amazon Data Scientist 

The major skills required to become an Amazon data scientist are as follows: 

  1. Functional programming languages and statistical analysis packages, including R, Stata, MATLAB, SQL, Python, C++, and Java.
  2. Data mining and database expertise using large-scale, complex datasets in an organization setting.
  3. Practical knowledge of data extraction, data analysis, and communications in an analytical operation.
  4. It is preferred to have candidates with a Ph.D. in statistics, data analysis, ML, or a related subject. OR A master’s degree in economics, computer engineering, physics, mathematics, statistics, or another related field, or equivalent work experience.

How to Get Relevant Skills? 

An individual who wants to work as an Amazon data scientist needs expertise in several technical skills apart from master’s and Ph.D. degrees. Various courses and programs on the internet can help you gain expertise in specific fields, but choosing the best course can be overwhelming. The certified AI and ML Blackbelt Plus program by Analytics Vidya can help you gain important skills needed to become an Amazon data scientist. The features of this incredible program include the following: 

  • 500+ learning hours 
  • More than 50 real-world projects
  • Extensive curriculum with the latest technologies
  • One-on-one mentorship sessions 
  • Expertise in 22+ tools 
  • Resume and interview preparation
  • Create your portfolio 
  • More than 35 assignments and worksheets 
Source: Inside Amazon India

Different Data Science Roles at Amazon 

The different roles for working in Amazon data science are as follows: 

Data Analyst

The primary responsibilities of this position include forecasting, identifying strategic possibilities, and providing knowledgeable business insights. Business intelligence is centered around making forecasts and identifying fresh prospects in business planning. It can often be necessary to have knowledge of data warehousing and use Tableau and other data visualization platforms.

Check out – How to Become a Data Analyst?

Data Scientist

The most prevalent position is that of the data scientist, which requires delving into vast data sets to create sizable experimental frameworks and simulations, create optimization techniques, and utilize cutting-edge technologies across Amazon. Amazon data scientists work with a worldwide data set.

Check out – How to Become a Data Scientist?

Data Engineer

As a Data Engineer in the Amazon Business Data Analytics and Insights (ABDAI) team, an applicant will work in one of the world’s biggest cloud-based data lakes. They must be knowledgeable about the architecture of business data warehousing solutions employing various technologies. 

Check out – How to Become a Data Engineer?

Machine Learning Research Scientist

This role concentrates on pioneering studies in areas like social networking sites, video recommendations, neural networks, NLP, and streaming data evaluation. Qualified researchers for this position typically have extensive artificial intelligence understanding and are typically well-known experts with PhDs in related subjects.

Amazon Interview Process

The Amazon data science interview procedure includes an initial phone interview, technical phone screens, behavioral phone screens, and a final in-person interview. 

#Round 1 – HR Interview

A 20–30 minute basic discussion with an Amazon recruiter or HR representative to discover further information about you, your background, and your potential in the role. The hiring supervisor will review the details and explain how the interview process will proceed.

#Round 2 – Technical Screening

During the Amazon data scientist technical interview, the company assesses a candidate’s proficiency in programming languages like Python, Java, and SQL. It ensures that an applicant has expertise in math, statistics, data mining, data extraction processes, and the complete data flow. It also tests candidates’ proficiency with data visualization and machine learning tools. You can prepare for two coding questions, one SQL question, a machine learning conceptual query, and an algorithmic coding style question on the technical screen.

#Round 3 – In-Person Interview

You might undergo 5–6 in-person or online interviews while applying for this position. They will last between 45 and 60 minutes and will evaluate you on various facets of the job. Behavioral screening aims to better understand candidates, evaluate their communication abilities, assess how they address problems, and observe instances of the 14 leadership principles in practice.  

The interview process at Amazon for Data Science profiles can be different. You can refer to the job roles and their selection process here!

Common Interview Question

Amazon data scientist interview questions
Source: Teamrora

Numerous questions can be asked to applicants in an Amazon data science interview. Some of the questions that can be asked for the position of Amazon data scientist are as follows: 

  1. What do discriminative and generative algorithms do? What are their advantages and disadvantages? What kinds of algorithms are typically employed, and why?
  2. Write a program to determine whether a smaller string may be formed from letters in a larger string provided a large string and a shorter string.
  3. Use an effective method to implement the intersection and union of two arrays. The elements in the two supplied arrays can be repeated, but not in the union and intersection arrays.
  4. Tell me about an instance when you resolved a customer’s issue, and describe any challenges you experienced. 
  5. Assume that you raise the minimum sample size per leaf in terminal nodes. What happens to the variance and bias in a random forest model?
  6. How does a linear model’s underlying assumption change in the instance of an outlier?
  7. How do you assess the findings of an OLS regression?
  8. List the five fundamental assumptions of linear regression.
  9. What algorithm would you apply to generate a list of randomized 1s and 0s if you were provided an unfair coin with a probability of heads less than or equal to.5?
  10. How is K-means implemented? Which distance measure would you use?

Tips on Clearing Amazon Interview Process 

Here are some tips that can help you during the interview for AWS data scientist or Amazon data scientist:

  1. Don’t forget to catch up on streamlining queries, solving algorithms, and knowing how numerous machine learning methods operate.
  2. Amazon evaluates every applicant based on its 14 principles of leadership. Commit to jot down all 14 leadership concepts. Evaluate your prior projects or accomplishments and how you have implemented those principles.
  3. Evaluate your understanding of modeling, machine learning, and business-related issues. Amazon will probably give you a variety of hypothetical case scenarios in which they’ll want you to use machine learning in a professional setting.
  4. Don’t forget to explain your strategy and thought process as you work through the coding and SQL questions. Maintain a casual tone during the interview and express your ideas as you answer the questions.
  5. Make a brief yet powerful elevator presentation. Discuss a topic that isn’t included in your resume. Maintain your composure and make a good impression throughout the call. Prepare a concise response to the questions “Why Amazon?” and “What regarding the job that you are applying for interests you?”.

Amazon Data Scientist Earnings Level

Amazon data scientists’ salaries vary depending on experience, skill level, education, and region. If someone is eligible for yearly bonuses or stock awards, their average total pay may also differ significantly. The salary for Amazon data science jobs in some countries is as follows:

CountryAverage Annual Amazon Data Scientist Salary
India₹15,47,601 
USA$126,968
UAEAED 240,000
UK£71,883 
AustraliaA$80,000 – A$100,000 
Source: Glassdoor

Conclusion

With its incredible workforce from all divisions, Amazon is one of the largest businesses dominating the globe. Data scientists will acquire all the required skills and expertise working for an organization like Amazon. Discovering solutions to fresh, exciting business problems is the core of data science. Amazon data scientists are on the ideal path and have the best job possibilities.

Kickstart your journey towards Amazon Data Scientist career by taking up our course on certified AI and ML Blackbelt Plus program!

Frequently Asked Questions 

Q1. How to become a data scientist for Amazon?

A. An applicant needs to have a Ph.D. in machine learning, data analysis, statistics, etc., to be considered for a position as a data scientist at Amazon. Their arithmetic abilities will be just as important as their programming abilities.

Q2. Does Amazon hire a fresher data scientist?

A. Amazon’s hiring standards are very demanding regarding the technological field. To become an Amazon Data Scientist, you must have a Ph.D. in statistics, data analysis, ML, or a related study. Your mathematics abilities will be equally essential as your technical coding ability. Strong analytical abilities, business data tools, ETL, statistical modeling, and data mining are all needed.

Q3. What do data scientists do at Amazon?

A. Amazon data scientists serve as a bridge between the company, clients, and technological solutions. They offer insightful analysis that helps make choices and develop client solutions by modeling and converting datasets.

Q4. What is the salary of Amazon ads data scientists?

A. The salary starts at $88,123 per year and, depending on the seniority, can go up to $167,246 annually. 

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