Google is a major technology company with a wide and firm foothold in the information technology space. All decision making at Google is data-driven. So, collecting and making sense of vast data sets is of core value to the company. It is here that the role of data scientists comes into play for Google.
As a Data Scientist, you will evaluate and improve Google's products. You'll collaborate with a multi-disciplinary team of Engineers and Analysts on a wide range of problems, bringing analytical rigour and statistical methods to the challenges of measuring quality, improving consumer products, and understanding the behaviour of end-users, advertisers, and publishers.
Data Scientist average salary at Google:
- Entry level salary :$150,000.
- Senior positions :$485,000.
- Median salary :$300,000 with base component being $175,000, stock component being $100,000 and bonus being $25,000.
Roles and responsibilities
- Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods.
- Conduct end-to-end analysis, including data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
- Build and prototype analysis pipelines iteratively to provide insights at scale.
- Develop a comprehensive understanding of Google data structures and metrics, advocating for changes where needed.
- Interact cross-functionally with a variety of people and teams. Work closely with Engineers to identify opportunities, design, and assess improvements to Google products.
- Make business recommendations (e.g. cost-benefit, forecasting, experiment analysis) with effective presentations of findings at multiple levels of stakeholders through visual displays of quantitative information.
- Research and develop analysis, forecasting, and optimization methods to improve the quality of Google's user-facing products such as Android, YouTube, Chrome and others.
Preferred Skills and Qualifications
- Master's degree in a quantitative discipline or equivalent practical experience.
- 4 years of relevant work experience, including expertise with statistical data analysis such as linear models, multivariate analysis, stochastic models, and/or sampling methods.
- Experience with Machine Learning on large datasets.
- Experience with statistical software (e.g., R, Python, Julia, MATLAB, pandas) and database languages (e.g., SQL).
- Experience articulating business questions and using mathematical techniques to arrive at an answer using available data.
- Experience translating analysis results into business recommendations.
- Ability to select the right statistical tools given a data analysis problem.
- Willingness to both teach others and learn new techniques.
- Excellent leadership and communication skills
Once the candidate's resume is shortlisted, the interview process begins, broadly consisting of 3 stages:
- Initial Screen
- Technical Screen
- Onsite round
This is the preliminary interview, largely similar to the initial screen conducted at other tech companies such as Amazon, Apple, Microsoft etc. It's a telephonic interview round and lasts for about 30 minutes. Here, the recruiter asks some basic questions regarding your CV, your motivation for the role and company, and assesses your experiential and cultural fit for the company.
The technical screen is usually conducted via video conferencing with one of Google's Data Scientists. The questions asked in this interview generally focus on topics such as experimental design, statistics, and probability. You can also expect a few questions on machine learning, AI, deep learning, data science-based reasoning questions, and some coding questions (SQL, Python and Java). The interviewer may also deep dive into the technical aspects of your past research and work experience and discuss what problems you faced and your approach to solving them.
Most asked interview questions in the Technical screen
- What is the derivative of 1/x?
- Draw the curve log(x+10)
- How would you design a customer satisfaction survey?
- Tossing a coin ten times resulted in 8 heads and 2 tails. How would you analyze whether a coin is fair? What is the p-value?
- You have 10 coins. You toss each coin 10 times (100 tosses in total) and observe results. Would you modify your approach to the way you test the fairness of coins?
- Explain a probability distribution that is not normal and how to apply that?
- Why use feature selection? If two predictors are highly correlated, what is the effect on the coefficients in the logistic regression? What are the confidence intervals of the coefficients?
- Mean and Gaussian mixture model: what is the difference between K-means and EM?
Not feeling confident about the Technical Screen? Book a mock with our Google Data Scientist Experts→ Book a Mock interview here
The onsite interview is the final stage of the Google data scientist interview process. It consists of five 1:1 interviews, each around 45 minutes long, with Google's data scientists. Machine learning, AI, and mathematical topics such as statistics, probability, product interpretation, metrics and experimentation and modelling are primarily asked. The total duration of the onsite loop is roughly 5 hours with a one-hour lunch in between.
3 quick tips for the Onsite round:
- Let the interviewer know your approach to the problem, and not just the solution.
- Do not hesitate to ask clarifying questions to the interviewer.
- Keep an answer ready for the cliched "Why Google?" question by the company HR.
Most asked interview questions in the Onsite round
- When using the Gaussian mixture model, how do you know it is applicable? (Normal distribution)
- If the labels are known in the clustering project, how to evaluate the performance of the model?
- You have a google app and you make a change. How do you test if a metric has increased or not?
- Describe the process of data analysis?
- Why not logistic regression, why GBM?
- Derive the equations for GMM.
- How would you measure how much users liked videos?
- Simulate a bivariate normal
- Derive variance of a distribution
- How many people apply to Google per year?
- How do you build estimators for medians?
- If each of the two coefficient estimates in a regression model is statistically significant, do you expect the test of both together is still significant?
To sum it up, focus on preparing your CV well for the initial screen. For the technical screen, brush up on your statistics, probability and machine learning theory. Also, be prepared for a few coding questions in SQL/Java/Python. The onsite interview is the real challenge. Brush up on your Machine Learning, AI, and Algorithm concepts and mathematical topics such as statistics and probability exhaustively. With these things in mind, we are sure you will crack the Google Data Scientist interview.
Want to practise for the Onsite Round? Book a mock interview with our Gogle Data Scientist Experts→ Book a Mock interview here
How long does the interview last?
The different stages of the interview are of different lengths. The initial screen is of 30 minutes while the the technical screen lasts for about an hour. The onsite round is the longest of all, and can cover a full day.
How many rounds are there in the Google Data Scientist Interview
The Google data Scientist interview has 3 rounds, namely Initial Screen, Technical Screen, and the Onsite round.