Is HireStepX free to use?

Yes. Start with 2 full AI mock interviews, complete with real-time feedback, scores, and detailed performance reports. No credit card required.

How does the AI mock interview work?

Upload your resume, pick your target company and role, and choose from 10 interview types. The AI interviewer asks role-specific questions via voice, listens to your answers, asks follow-up questions, and delivers scored feedback after each session.

What types of interviews can I practice?

10 types: Behavioral, Technical, Strategic, Case Study, Campus Placement, HR Round, Panel, Management, Salary Negotiation, and Government/PSU. Each with 3 difficulty levels and mini or full session options.

Can I practice for specific companies like TCS, Infosys, or Google?

Yes. We support 200+ target companies including Google, Amazon, TCS, Infosys, Flipkart, Razorpay, McKinsey, Deloitte, and more, each with distinct interview patterns.

How is this different from ChatGPT or practicing with friends?

ChatGPT is text-only with no voice, no scoring, no resume integration, and no progress tracking. HireStepX is a purpose-built interview simulator: voice-based, resume-personalized, with detailed analytics.

Is my interview data private and secure?

Yes. Data is encrypted via Supabase with row-level security. Recordings and transcripts are never shared with employers or third parties. Delete everything anytime from Settings.

Does HireStepX work on mobile?

Yes, on any modern browser. For the best experience during mock interviews, use a laptop or desktop with a microphone.

How much does it cost compared to a career coach?

A single coaching session typically costs ₹3,000-10,000. HireStepX is ₹9 per session or ₹39/month for 5 sessions (Sprint Pack) with full AI coaching and analytics, available 24/7.

What is the recruitment process at Google?

The typical Google recruitment process has 4 stages: Recruiter screen and role alignment → Technical phone screen on statistics, SQL, and analytics → Onsite loop: analytics case, statistics, ML fundamentals, and behavioural → Team match and offer.

What are the interview rounds at Google?

Google typically conducts 4 interview rounds: Round 1 (45-60 min): statistics and SQL screen.; Round 2 (45-60 min): product-analytics and experiment-design case.; Round 3 (45-60 min): ML fundamentals and statistical-reasoning round.; Round 4 (45 min): behavioural and Googleyness round..

What framework should I use for Google technical interviews?

HireStepX recommends the Product-Sense-and-Rigour framework for this type of interview: Pair rigorous experiment design and guardrail-metric thinking with clean statistical derivations and ML fundamentals, always tied to a real product decision

Given a stream of integers, design a data structure that returns the median in O(log n).

To answer this question well, HireStepX recommends the Product-Sense-and-Rigour approach: Pair rigorous experiment design and guardrail-metric thinking with clean statistical derivations and ML fundamentals, always tied to a real product decision Ground your answer in a specific real example from your own experience.

You have a service that's slow only on Tuesdays. Walk me through how you'd debug.

To answer this question well, HireStepX recommends the Product-Sense-and-Rigour approach: Pair rigorous experiment design and guardrail-metric thinking with clean statistical derivations and ML fundamentals, always tied to a real product decision Ground your answer in a specific real example from your own experience.

You're the Staff Engineer rolling out Cursor/Copilot to a team of 25 with mixed seniority. What's your 90-day plan and how do you measure success?

To answer this question well, HireStepX recommends the Product-Sense-and-Rigour approach: Pair rigorous experiment design and guardrail-metric thinking with clean statistical derivations and ML fundamentals, always tied to a real product decision Ground your answer in a specific real example from your own experience.

You launch a feature and DAU rises but session length falls. Design an analysis to decide whether the feature is net positive. What metrics and tests do you use?

To answer this question well, HireStepX recommends the Product-Sense-and-Rigour approach: Pair rigorous experiment design and guardrail-metric thinking with clean statistical derivations and ML fundamentals, always tied to a real product decision Ground your answer in a specific real example from your own experience.

Explain the bias-variance trade-off and how you'd diagnose whether a model is underfitting or overfitting from its learning curves.

To answer this question well, HireStepX recommends the Product-Sense-and-Rigour approach: Pair rigorous experiment design and guardrail-metric thinking with clean statistical derivations and ML fundamentals, always tied to a real product decision Ground your answer in a specific real example from your own experience.

Derive why maximum-likelihood estimation for linear regression with Gaussian noise gives the least-squares objective.

To answer this question well, HireStepX recommends the Product-Sense-and-Rigour approach: Pair rigorous experiment design and guardrail-metric thinking with clean statistical derivations and ML fundamentals, always tied to a real product decision Ground your answer in a specific real example from your own experience.

What does the Google data-scientist interview cover?

Product analytics and experimentation (A/B tests, metric trade-offs, guardrail metrics), statistics (bias-variance, MLE, hypothesis testing), ML fundamentals, and SQL.

How important is product sense for Google data scientists?

Very. Many questions frame statistics inside a product decision, like whether a feature that raises DAU but cuts session length is net positive. Rigorous, decision-oriented analysis is rewarded.

How do I prepare for Google data-scientist interviews?

Practise A/B testing and metric trade-offs, core statistics and clean derivations, ML fundamentals like bias-variance, and SQL, and rehearse narrating your reasoning to a product decision.

← All companies·Google·Technical

Google Data Scientist Interview Questions India 2026

Google's data-scientist interviews blend product analytics, statistical depth, and ML fundamentals, and product sense is weighted as heavily as maths.

Practice with AI voice feedback 6 questions · Technical · 2 sessions free

In 2026 expect analytics-and-experimentation questions (a feature raises DAU but cuts session length, so is it net positive, and how would you decide), statistical foundations (bias-variance diagnosis from learning curves, deriving least-squares from Gaussian-MLE), and applied ML reasoning, alongside SQL. Interviewers reward rigorous experiment design, guardrail-metric thinking, and clean derivations you can narrate. Whether you target Product Analytics or a research-leaning role, the bar is connecting statistics to real product decisions. Prepare A/B testing, metric trade-offs, core statistics, and ML fundamentals, and practise reasoning aloud.

About Google

Alphabet's flagship: search, advertising, Android, Cloud, YouTube, AI infrastructure.

Products: Search · Ads · Android · Google Cloud Platform · YouTube · Workspace · Pixel
Competitors: Microsoft · Amazon (cloud, ads) · Meta (ads) · Apple (mobile) · OpenAI/Anthropic (AI)

Google recruitment process

01
Recruiter screen and role alignment
02
Technical phone screen on statistics, SQL, and analytics
03
Onsite loop: analytics case, statistics, ML fundamentals, and behavioural
04
Team match and offer

What to expect in each round

01
Round 1 (45-60 min)
statistics and SQL screen.
02
Round 2 (45-60 min)
product-analytics and experiment-design case.
03
Round 3 (45-60 min)
ML fundamentals and statistical-reasoning round.
04
Round 4 (45 min)
behavioural and Googleyness round.

Technical questions Google asked

Sourced from 2+ candidate post-mortems. Hit Practice to answer any one with AI voice feedback.

01
Hard
Given a stream of integers, design a data structure that returns the median in O(log n).
Practice free →
02
Medium
You have a service that's slow only on Tuesdays. Walk me through how you'd debug.
Practice free →
03
Hard
You're the Staff Engineer rolling out Cursor/Copilot to a team of 25 with mixed seniority. What's your 90-day plan and how do you measure success?
Practice free →
04
Hard
You launch a feature and DAU rises but session length falls. Design an analysis to decide whether the feature is net positive. What metrics and tests do you use?
Practice free →
05
Medium
Explain the bias-variance trade-off and how you'd diagnose whether a model is underfitting or overfitting from its learning curves.
Practice free →
06
Hard
Derive why maximum-likelihood estimation for linear regression with Gaussian noise gives the least-squares objective.
Practice free →

Reading won't get you hired

Stop reading, start answering.

The AI asks Google-style questions, listens to your voice, and scores your answer in two minutes. 2 sessions free, no card.

Start free practice

← All companies·Google·Technical

Google Data Scientist Interview Questions India 2026

Google's data-scientist interviews blend product analytics, statistical depth, and ML fundamentals, and product sense is weighted as heavily as maths.

Practice with AI voice feedback 6 questions · Technical · 2 sessions free

About Google

Alphabet's flagship: search, advertising, Android, Cloud, YouTube, AI infrastructure.

Products: Search · Ads · Android · Google Cloud Platform · YouTube · Workspace · Pixel
Competitors: Microsoft · Amazon (cloud, ads) · Meta (ads) · Apple (mobile) · OpenAI/Anthropic (AI)

Google recruitment process

01
Recruiter screen and role alignment
02
Technical phone screen on statistics, SQL, and analytics
03
Onsite loop: analytics case, statistics, ML fundamentals, and behavioural
04
Team match and offer

What to expect in each round

01
Round 1 (45-60 min)
statistics and SQL screen.
02
Round 2 (45-60 min)
product-analytics and experiment-design case.
03
Round 3 (45-60 min)
ML fundamentals and statistical-reasoning round.
04
Round 4 (45 min)
behavioural and Googleyness round.

Technical questions Google asked

Sourced from 2+ candidate post-mortems. Hit Practice to answer any one with AI voice feedback.

01
Hard
Given a stream of integers, design a data structure that returns the median in O(log n).
Practice free →
02
Medium
You have a service that's slow only on Tuesdays. Walk me through how you'd debug.
Practice free →
03
Hard
You're the Staff Engineer rolling out Cursor/Copilot to a team of 25 with mixed seniority. What's your 90-day plan and how do you measure success?
Practice free →
04
Hard
You launch a feature and DAU rises but session length falls. Design an analysis to decide whether the feature is net positive. What metrics and tests do you use?
Practice free →
05
Medium
Explain the bias-variance trade-off and how you'd diagnose whether a model is underfitting or overfitting from its learning curves.
Practice free →
06
Hard
Derive why maximum-likelihood estimation for linear regression with Gaussian noise gives the least-squares objective.
Practice free →

Reading won't get you hired

Stop reading, start answering.

The AI asks Google-style questions, listens to your voice, and scores your answer in two minutes. 2 sessions free, no card.

Start free practice