Designed and taught by FAANG+ Data and Research Scientists to help you land your dream job in FAANG and Tier-1 companies. In a span of 15 weeks, we will cover everything you need to nail your next Data Science interview.
Data Science Engineers!
Get interview-ready with lessons by FAANG+ Data Scientists
Students who chose to uplevel with IK got placed at
Sayan Banerjee
Data Scientist II
Siva Karthik Gade
Software Development Engineer
Sai Marapa Reddy
SWE, Machine Learning
Safir Merchant
SWE, Machine Learning
Mike Kane
Lead Data Engineer, Analytics
Akshay Lodha
Data Engineering & Analytics
Jaime Lichauco
Database Engineer
Anju Mercian
Data Engineering Consultant
Alokkumar Roy
Data Engineer
13,500+
Tech professionals trained
$1.267M
Highest offer received by an IK alum
53%
Average salary hike received by alums
Best suited for
Current or Former Data Scientists, Research Scientists, Applied Scientists, etc.
Software Engineers working on Statistical or ML models
Data Analysts, Business Intelligence Analysts, Product Analysts, Statisticians, etc.
Why choose this course?
Program designed by FAANG+ leads
Covering data structures, algorithms, interview-relevant topics, and career coaching
Individualized teaching and 1:1 help
Technical coaching, homework assistance, solutions discussion, and individual session
Mock interviews with Silicon Valley engineers
Live interview practice in real-life simulated environments with FAANG and top-tier interviewers
Personalized feedback
Constructive, structured, and actionable insights for improved interview performance
Career skills development
Resume building, LinkedIn profile optimization, personal branding, and live behavioral workshops
50% Money-Back Guarantee*
If you do well in our course but still don't land a domain-relevant job within the post-program support period, we'll refund 50% of the tuition you paid for the course.*
Our highly experienced instructors are active hiring managers and employees at FAANG+ companies and know exactly what it takes to ace tech and managerial interviews.
Andrew Treadway
Research Data Scientist
9+ years experience
Learn more
Omkar Deshpande
Head of Curriculum
15+ years experience
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Nick Camilleri
Head of Career Skills Devp. and Coaching
10+ years experience
Learn more
Qiuping Xu
Principal Scientist
9+ years experience
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Abdul Salama
Manager, AI Platform
12+ years experience
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Bharath Srikanth
Senior Data Scientist
7+ years experience
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Bhuvan Venkatesh
Data Scientist
5+ years experience
Learn more
A typical week at Interview Kickstart
This is how we structure and organize your interview prep with our high-quality, content-rich course. Our learners devote 10 to 12 hours per week to this course.
Thu
Foundation content
Get high-quality videos and course material for next week’s topic
Consists of introduction to fundamentals, interview-relevant topics and case studies
Assignment review session
Solve questions and case studies based on the assignment shared with you
Sun
Online live sessions
Attend 4-hour sessions hosted by Lead Data Scientists and Research Scientists from FAANG+ companies
Discuss open-ended questions and problem-solving strategies
Get pro tips to solve challenging interview problems
Mon-Wed
Practice problems & assignments
Practice the concepts taught in live sessions to solve assignment questions
Live doubt-solving with FAANG+ Data Science instructors
Learn about the hiring process and interview experiences at FAANG+ companies from the instructors
Every day
1:1 access to instructors
Personalized coaching from FAANG+ DS instructors
Individualized and detailed attention to your questions
Common recursion- and backtracking-related coding interview problems
3
Trees
Dictionaries & Sets, Hash Tables
Modeling data as Binary Trees and Binary Search Tree and performing different operations over them
Tree Traversals and Constructions
BFS Coding Patterns
DFS Coding Patterns
Tree Construction from its traversals
Common trees-related coding interview problems
4
Graphs
Overview of Graphs
Problem definition of the 7 Bridges of Konigsberg and its connection with Graph theory
What is a graph, and when do you model a problem as a Graph?
How to store a Graph in memory (Adjacency Lists, Adjacency Matrices, Adjacency Maps)
Graphs traversal: BFS and DFS, BFS Tree, DFS stack-based implementation
A general template to solve any problems modeled as Graphs
Graphs in Interviews
Common graphs-related coding interview problems
5
Dynamic Programming
Dynamic Programming Introduction
Modeling problems as recursive mathematical functions
Detecting overlapping subproblems
Top-down Memorization
Bottom-up Tabulation
Optimizing Bottom-up Tabulation
Common DP-related coding interview problems
Data Science
7 weeks
7 live classes
1
SQL Programming (interview-focused concepts and questions)
Derive business insights for a food delivery app by writing SQL queries
Comprehensive coverage of topics from intermediate-level concepts such as case statements and subqueries to advanced SQL functions such as joins and analytical functions
Application of window functions as lead, lag functions to evaluate day-over-day insights on business performance
Use rank and dense rank functions to understand merchants’ reach in the market
Complex SQL problems on customer-merchant pairwise dependence using a variety of functions and operators
Deep dive into joins, their type, and comparison of left join vs. right join vs. outer join vs. broadcast join
Thematic coverage of frequently asked interview problems through template problems
A step-by-step guide to what you can expect in an interview and how to tackle them in a time-constrained environment
2
Probability
Challenging combinatorial probability questions involving coin tosses, dice throws, cards, and balls (popular in FAANG+ interviews)
Dealing with bias: Given an outcome, finding the probability of the coin being biased
Interview questions on conditional probability and Bayes theorem: Given the statistics, what is the probability of success of an event
3
Distributions
Random variables, distributions, PDF, and CDF
Intriguing properties of normal distribution and related common interview questions
The application of normal distribution in various industries/fields such as finance, trading, etc.
Importance of normalization and standardization during data analysis
Central Limit Theorem and its real-life applications
Extensive coverage of distributions, including uniform distribution, binomial distribution, Poisson distribution, exponential, etc.
Relationships among probability distributions such as approximating binomial distribution to the normal distribution under the certain circumstances
Common FAANG+ interview questions on distributions:
Say you have X1 ~ Uniform(0, 1) and X2 ~ Uniform(0, 1). What is the expected value of the minimum of X1 and X2?
Suppose a fair coin is tossed 100 times. What is the probability there will be more than 60 heads?
The probability of a car passing a certain intersection in a 20-minute window is 0.9. What is the probability of a car passing the intersection in a 5-minute window?
4
Data Science Design: A/B testing
Hypothesis testing, develop null and alternative hypotheses
Familiarity with p-value and general misconceptions like p-value is the probability of the null hypothesis being false
How to find the confidence interval? What are Type-1 and Type-2 errors?
One side vs. Two side testing. When to use when?
T-test vs. Z-test: How can we test whether the avg. car speed on the highway exceeds 65mph with a significance level of 0.05?
Chi-square test and ANOVA (ANalysis Of VAriance)
Learn how FAANG+ companies do A/B testing for their business
Tough interview questions such as determining whether a new video recommendation algorithm has been better than the current one
Performance metrics: Answer business questions, such as opportunity estimation and gap analysis
Application of AUC-ROC, Accuracy, Precision, Recall, F-score, etc.
Interview-relevant strategies: What questions to ask an interviewer? How to structure your solution?
5
Regression, MLE, EM, and MAP
Regression: Investigate the relationship between two variables
Assumption of Linear Regression and common interview questions such as what if one of the assumptions doesn’t work?
Least Square Estimator vs. Maximum Likelihood Estimator: Under what conditions they are the same?
Ridge Regression vs. Lasso Regression: Which regression can make a certain coefficient to exactly zero and how?
Likelihood function: Measure how well observed data fits the assumed distribution
Maximum Likelihood Estimation: A car speed on the highway follows a normal distribution: N(μ,25), After observing the n car speed, what is the MLE for μ?
Expectation-Maximization: Understand it through the example of Gaussian Mixture Models
Maximum a posteriori (MAP) and how it’s different from Maximum Likelihood Estimation
6
Supervised Machine Learning
Defining the steps for data preprocessing with the help of intuitive examples
Best practices of data type identification, data quality correction, feature engineering, dimensionality reduction
Model training and the importance of training, validation, and test datasets
Interview-focused concepts like objective functions and evaluation metrics are revisited to help understand the topic as a whole
Optimization techniques like Gradient Descent, SGD, and Adam Optimizer with challenging questions
Describing interview-focused Supervised Machine Learning Algorithms like Logistic Regression, Naive Bayes, kNN, and SVM
Learn to break down problems with logistic regression and understand issues with logistic regression
Limitations of Naive Bayes explaining why it is naive
Visualizing the KNN algorithm in the context of classification and regression
Graphically distinguishing between various cases for classification using the Support Vector Machine algorithm
SVM kernel tricks and related interview questions
Interview questions on kernel: Can it be used with KNN?
The intuition behind the decision tree: how to arrive at a decision by asking a series of binary questions
Building a decision tree from scratch
Overfitting and underfitting in the context of machine learning algorithms
Bagging vs. Boosting
Interview questions: Why random forest? Why is it random? Common problems with decision trees and random forest
7
Unsupervised Machine Learning
Defining recommendation systems through examples from video streaming and online shopping
Illustration of different approaches to build recommendation systems like collaborative filtering, content-based filtering, and hybrid approaches
Drawbacks of item-based recommenders and why to use matrix factorization
Singular Value Decomposition and other alternatives for SVD
Classify the measure of similarity of data points by using Euclidean, Manhattan, and Cosine Similarity
Explain clustering by describing Gene Expression and Image Segmentation
Graphically depicting the K-Means Algorithm and how to choose the value of K
Understand DBSCAN Graphically depicting the K-Means Algorithm and how to choose the value of K
An algorithm and its parameters in detail and when it is preferred
Interview questions based on the preference of K-Means and DBSCAN Algorithm
Explore PCA and how to use it for Dimensionality Reduction
Learn to compute principal components iteratively and by using eigenvalues and eigenvectors
8
Deep Learning
Define Common Activation Functions and the advantages of using them
Understand neural networks with emphasis on interview questions such as the strategy of trying learning rate, linear or logarithmic scale
How do forward propagation and backward propagation work?
Dense Neural Network (DNN) on image processes and advantage of using CNN over DNN
Defining CNN Architecture: Kernels, Pattern Finding, and Feature Map
Common interview questions on CNN
Implementation of CNN using Tensorflow
Learn Dropout: Is dropout used in the test dataset?
Why RNN over N-gram models?
RNN Architecture, backward propagation over time, covering interview questions such as: what is exploding vs. vanishing gradient? Does RNN suffer both?
Bidirectional RNN (BiRNN) and Stacked RNN
Advantages of using BiRNN
How to go from Naive RNN to Long short-term memory (LSTM)
UpLevel will be your all-in-one learning platform to get you FAANG-ready, with 10,000+ interview questions, timed tests, videos, mock interviews suite, and more.
Mock interviews suite
On-demand timed tests
In-browser online judge
10,000 interview questions
100,000 hours of video explanations
Class schedules & activity alerts
Real-time progress update
11 programming languages
Get upto 15 mock interviews withhiring managers
What makes our mock Interviews the best:
Hiring managers from Tier-1 companies like Google & Apple
Interview with the best. No one will prepare you better!
Domain-specific Interviews
Practice for your target domain - Data Science
Detailed personalized feedback
Identify and work on your improvement areas
Transparent, non-anonymous interviews
Get the most realistic experience possible
More about mock interviews
Career impact
Our engineers land high-paying and rewarding offers from the biggest tech companies, including Facebook, Google, Microsoft, Apple, Amazon, Tesla, and Netflix.
Akshay Lodha
Data Engineering & Analytics
Placed at:
The experience with IK was phenomenal, it was totally worth it. After so many years I was interviewing and IK helped me a lot in orienting myself and to get into the rhythm. Had a transition from Goldman Sachs to Facebook. IK mentors guided me and told me not to worry about the preparation part and to focus on upskilling myself. That really made a huge difference.
Sayan Banerjee
Data Scientist II
Placed at:
Interview Kickstart has a very nice community. Everybody is very knowledgeable. Omkar is amazing. The way that he explains things is so great that it also taught me how to explain steps in problem solving well. Interview Kickstart's way of identifying patterns in a problem is the most effective part for me. Lastly, with the community, it enabled me to have a network of people that I can see different perspectives, and approaches to reaching solutions.
Siva Karthik Gade
Software Development Engineer
Placed at:
IK offers high-quality study material, knowledgeable and patient instructors working at industry-leading companies, well-paced live classes + tests + review sessions, always available technical + career coaches, mock interview support from the best interviewers in the respective fields. IK brings together people with same the ambition (on their platform, UPLEVEL) to guide and inspire each other
Nadha Gafur
Machine Learning Engineer
Placed at:
It has been a great learning experience. The structure is really good and the materials as well. The lectures and live class pre-reading material is very informative and engaging.
Sai MarapaReddy
Software Engineer
Placed at:
I completed IK’s program and got offers from a couple of FAANG companies. Why you should take this course: It is well tested and the focus is more on the concepts/templates rather than approaching one problem at a time. You will meet peers who have similar aspirations. You can make groups and help yourselves.
Safir Merchant
Machine Learning Software Engineer
Placed at:
I liked the course that IK provided a lot. IK provided all the knowledge on a variety of topics that helped me prepare for coding interviews. The mock interviews were really great. Landing a job at my desired company has been a great pleasure.
How to enroll for the Data Science Interview Course?
Learn more about Interview Kickstart and the Data Science Interview course by joining the free webinar hosted by Ryan Valles, co-founder of Interview Kickstart.
A Free Guide to Kickstart Your Data Science Career at FAANG+
From the interview process and career path to interview questions and salary details — learn everything you need to know about Data Science careers at top tech companies.
The typical Data Science interview process at FAANG and other Tier-1 companies looks like:
One coding round: Easy to medium Leetcode problems or Python-based Data Manipulation and Wrangling questions. SQL is often a part of these rounds.
Behavioral round: Open-ended questions to gauge if you're a "good fit” for the company.
3-4 on-site rounds:
One problem-solving/discussion round
One take-home assignment round
1-2 domain rounds
What to Expect at Data Scientist Interviews
1
One coding round
Easy to medium Leet code problems or Python-based Data Manipulation and Wrangling questions. SQL is often a part of these rounds.
The SQL round is pretty standard across all the FAANG+ companies. You’ll be asked to solve problems using common clauses such as JOINS, WHERE, and GROUP BY.
Google tends to focus more on Statistical coding, some Data Analysis, and SQL since the company handles vast data sets.
2
One problem-solving/discussion round
Inclined towards discussing your work experience, past projects, and problem-solving with a mix of statistics, coding, probability, and some quantitative aptitude questions.
Facebook (Meta) focuses more on real-world data problems. So, prepare accordingly and provide concise answers when asked to elaborate on statistical terms.
3
One take-home assignment round
Some companies give a dataset and inference-based questions to judge problem-approach/deduction skills as part of take-home assignments. The usual deadline is 24-48 hours.
For the take-home assignment given by Apple, you’ll only be provided with three days. It will probably be a Machine Learning problem, and you’ll have to develop a model and give a prediction using the dataset.
4
1-2 domain rounds
This round demands a deep dive into Data Science fundamentals. Interview questions in these rounds typically focus on designing experiments to meet certain business goals, A/B testing, and ML algorithms.
You’ll need to be clear about how you frame the problem, the metrics you use, A/B testing, technical trade-offs, and so on, along with the required data analysis.
5
One behavioral round
You can expect Data Science interview questions on your job experience and discussions on past projects along with open-ended questions to gauge if you're a "good fit.”
When applying at Google, ensure that you have an answer for “Why Google?”. Such questions are asked at all FAANG+ companies.
Thoroughly research each company's leadership principles and develop answers in the form of a story based on those characteristics.
Data Science interview questions are based on various topics. You can answer them if you identify the common fundamentals.
Try answering these Data Science interview questions:
1
Data Science Interview Questions on Coding
Write a code that takes a number from the user and outputs all Fibonacci numbers less than the user input.
Given: The CDF of a distribution. Find: The mean.
Given: Two numbers a, b ;a<b. Find: Output of f(a,b) = g(a) + g(a+1) +g(a+2) +…+ g(b-2) + g(b-1) + g(b), where g(x) is defined as all Fibonacci numbers less than x.
Given: A number X. Find: The smallest sum of two factors (a, b) of X.
Given: Person A decides to go on a skydiving trip. Based on his research, the probability of a glitch resulting in death is 0.001. Find: The probability of death if A goes on 500 skydives.
2
Domain-specific Data Science Interview Questions
How do you define the ROC curve?
What is meant by the true positive rate and false-positive rate?
What are the steps involved in making a decision tree?
Given a data set consisting of variables with more than 30 percent missing values, how will you deal with them?
Define dimensionality reduction and what are its advantages?
Explain how you would calculate eigenvalues and eigenvectors of the following 3x3 matrix.
How to deal with unbalanced binary classification?
What is the difference between normalization and standardization?
Why does data cleaning play a vital role in the analysis?
3
Data Science Interview Questions on Behavioral Skills
Walk us through a project you’re very proud of.
Have you ever used data science to inform a business decision?
How well do you communicate technical concepts to non-technical team members?
How have you used data to elevate the experience of a customer or stakeholder?
Describe when you had to clean and organize a big data set.
If you want to go over some more Data Science interview questions, read:
The Data Scientist career paths have been booming, and this trend is expected to continue in the upcoming years. Our Data Science Interview Course can help you gain the required skills to land the best job offers in top tech companies.
1
Data Science Career Roadmap
A Data Scientist’s career path features two main career tracks:
Individual Contributor roles
Management roles
Data Scientist Career Path — Individual Contributor (IC) Roles
Individual contributors in the Data Scientist’s career path work on core data science tasks such as programming, creating models, coding, solving complex problems, and getting hands-on with the technical aspects of data science jobs.
Advanced or deep technical or hard skills are key to developing an IC Data Scientist career path.
Typically, the Data Scientist career path for an individual contributor (IC) follows this progression:
Data Scientist 1 → Data Scientist 2 → Senior Data Scientist → Staff Data Scientist → Sr. Staff Data Scientist → Principal Data Scientist
2
Data Scientist Career Path — Management Roles
A managerial role in a Data Scientist’s career path involves management tasks such as leadership, building relationships, conflict resolution, etc.
For software engineering managerial roles, a conceptual understanding of the technologies used is sufficient to perform managerial tasks. In contrast, Data Science Managers must have a working knowledge of the technologies used.
Communication, leadership, collaboration, and other soft skills are essential for developing a Data Scientist career path in a management role.
Typically, the Data Scientist career path in management follows this progression:
Data Scientist 1 → Data Scientist 2 → Senior Data Scientist → Data Science Manager → Sr. Data Science Manager
To understand the career trajectory of a Data Scientist better, read:
Qualifications Required to Become a Data Scientist
Depending on where you are in your Data Scientist career path, you will need the following educational degrees:
Bachelor’s/Master’s degree in Computer Science, Software Engineering, or a related field; Bachelor’s degree for an entry-level position and a Master’s degree for higher-level Data Scientist positions
Ph.D. in a relevant field is preferable and often a prerequisite for advanced or research and development positions.
You can also obtain professional certifications in the skills needed to pursue a career in Data Science. Some of the top Data Science certifications customized for Software Engineers and Software Developers to uplevel your Data Scientist career path are:
Tensorflow Developer Certification
Google Professional Data Engineer Certification
Amazon AWS Big Data Certification
Microsoft Certified Azure AI Fundamentals
SAS Certified AI & Machine Learning Professional
4
Job Roles and Responsibilities of a Data Scientist
Based on the experience and job profile, the different job responsibilities of Data Scientists have been put together in the table given below:
Data Scientist’s Job Responsibilities by Role
Role
Experience Required
Job Responsibilities
Junior-level Data Scientist
Internship/independent projects
Develop experience working on existing code, programs, models to enhance efficiency, effectiveness, quality, and outcomes.
Mid-level Data Scientist
2+ years
Create and implement basic models and make presentations for feedback; develop technical expertise, and learn all about operations and various facets of data science projects.
Senior Data Scientist
5+ years
Strong technical competence in data science projects; lead projects; good business sense, communication, interpersonal skills; create operational impact; perform at scale, deepen technical expertise, widen interpersonal skill set.
Senior Principal/Staff Scientist
8 - 10 years
Advanced conceptual and practical technical expertise; provide technical direction and at scale; create business organizational impact; deep business acumen; identify business opportunities and enable teams to solve complex problems.
Data Science Manager
5+ years
Manage small teams; strong project management skills; strong interpersonal and people management skills; management experience.
Senior Data Science Manager
8 - 10 years
Manage large teams; excellent interpersonal and people management skills; lead large projects; strong technical skills; deep business acumen; create business impact; manage resources and develop talent.
5
Top Skills Needed to Become a Data Scientist
Data Mining and Data Wrangling
Machine Learning and Artificial Intelligence
Python, R, C++
SQL, Pig, Hive
Predictive Modeling
Math and Statistics — Linear Algebra, Bayes Theorem, Geometry, Multivariable Calculus, Probability, Discrete Math, and Graph Theory
Tableau, Excel, Microsoft Power BI, Qlikview, and other Business Intelligence and Data Visualization tools
Hadoop, Apache Spark, Apache Kafka, TensorFlow, Pandas, Matplolib, Scikit-Learn, Spark MLib, Numpy, AWS Deep Learning AMI, and other data frameworks
Data Scientist Salary and Levels at FAANG+ Companies
The average Data Scientist's salary range is between $105,750 and $180,250 per year. However, total compensation varies considerably depending on the company, location, employee value, years of experience, and core skills.
We've listed the Data Scientist salary ranges for various FAANG+ companies below to give you a better idea of how they differ by level:
Facebook Data Scientist Salary
The different levels of Data Scientists at Facebook are:
IC3 (Associate Data Scientist): This is typically the level at which fresher Data Scientists or Software Engineers are hired.
IC4: Those hired at this level should have 3-5 years of industry experience. However, new grads can also be hired at this level, provided they can demonstrate skill and expertise.
IC5: Data Scientists hired at IC5 have at least 6-9 years of industry experience as they are required to lead complex projects on their own. Also considered the “terminal” level before a Data Scientist moves into the management domain as IC5 onwards, they perform more managerial responsibilities.
IC6: Most Data Scientists working at this level have almost 9+ years of experience.
IC7 and IC8: These levels require more than 10 years of experience.
Data Scientist Salary at Facebook
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
IC3
$168K
$127K
$29K
$14K
IC4
$222K
$155K
$48K
$19K
IC5
$302K
$184K
$90K
$29K
IC6
$404K
$218K
$142K
$44K
Amazon Data Scientist Salary
Amazon has its own Data Scientist job levels. They are:
L4: Entry-level Data Scientists with less than four years of experience pursuing advanced degrees. They need to be skilled in at least one scripting language and familiar with SQL.
L5: Mid-level Data Scientists have four to seven years of experience and may also have the title of Data Scientist II. At this level, Data Scientists usually have a Master’s degree with a good knowledge of coding.
L6: This level is for Data Scientists who have advanced degrees like Ph.Ds in Machine Learning, Natural Language Processing, etc., based on their area of specialization. The level includes several managerial positions as well.
L7: This level is for Principal Data Scientists with 10+ years of experience. These employees have several management responsibilities and essentially run the teams.
Data Scientist Salary at Amazon
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
L4
$175K
$132K
$26K
$21K
L5
$227K
$150K
$57K
$27K
L6
$315K
$160K
$140K
$19K
L7
$638K
$185K
$419K
$42K
Apple Data Scientist Salary
On average, the Apple Data Scientist’s salary is $170,871 per year in the US. It can range from $94k to $257k, depending upon your experience, location, skill sets, and many other factors.
Data Scientist Salary at Apple
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
ICT3
$207K
$149K
$41K
$17K
ICT4
$289K
$175K
$96K
$20K
ICT5
$395K
$220K
$145K
$33K
Netflix Data Scientist Salary
Unlike other companies such as Amazon and Apple, Netflix doesn’t have job levels. The company is known for hiring only senior professionals, like, Senior Data Scientists. However, even in this position, salary tends to differ.
Based on your experience and accomplishments, the Netflix Data Scientist salary ranges from $200,000 to $400,000. On average, a Senior Data Scientist at the company earns around $322,272 per year.
However, Netflix does offer a few opportunities for entry-level positions where the Data Scientist can earn around $127,000.
Data Scientist Salary at Netflix
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
Sr. SW. Engineer
$305K
$275K
$14K
$13K
Google Data Scientist Salary
With a user base spanning hundreds of millions, you can imagine how valuable Data Scientists must be to Google. The company employs almost 140,000 people globally, divided into teams; almost each of these teams has a Data Scientist.
There are nine different job levels at Google:
L3 (Data Scientist II): An entry-level position with 0-1 year of experience
L4 (Data Scientist III): Requires 2-5 years of experience
L5 (Senior Data Scientist): Requires over 5 years of experience
L6 (Staff Data Scientist): Requires over 8 years of experience
L7 (Senior Staff Data Scientist): Requires over 10 years of experience
L8-L11: Executive roles; only employees with considerable experience within Google are eligible for these positions
Data Scientist Salary at Google
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
L3
$158K
$119K
$32K
$14K
L4
$233K
$150K
$58K
$26K
L5
$307K
$181K
$96K
$32K
L6
$548K
$228K
$257K
$51K
FAQs on Data Science Interview Course
1
What does a Data Scientist do?
2
Is IK's Data Science Interview Course designed only for Data Scientists working in non-FAANG companies?
3
Why is system design not covered in IK’s Data Science Interview Course?
4
What skills are required to become a Data Scientist?
5
What qualifications are required to become a Data Scientist?
6
I am working as a Business Intelligence Analyst. Can this course help me to target roles such as Data Scientist?