Aggregated from 4 candidate experiences for ALL.
Tiger Analytics exhibits a structured yet role-specific hiring process, with a strong emphasis on technical rigor across coding, system design, and domain-specific knowledge. The company values problem-solving skills, adaptability to role-specific challenges, and alignment with project expectations. Rejection trends often highlight gaps in foundational skills, such as SQL proficiency or algorithmic depth, particularly for mid-level and senior roles.
The candidate went through three interview opportunities at Tiger Analytics over a span of one year. The process included coding rounds, technical rounds, an online assessment, and an HR round. The candidate was rejected after the first two attempts and rejected the offer in the third attempt due to existing offers and salary expectations.
A 45-minute coding round consisting of 2 questions, categorized as 1 Easy-Medium and 1 Medium. The candidate solved 1 question fully and partially solved the second.
A 45-minute technical round where the interviewer asked 2 coding questions (factorial of a number and matrix multiplication) and 1 complex SQL query. The interviewer also asked basic SQL and Python questions.
A 45-minute technical round where the interviewer asked 2 coding questions (Best time to buy stock and shorten a string based on frequency). The candidate explained the logic for the first question and optimized it. The second question was solved quickly. The interviewer also asked about the candidate's projects.
A 1-hour online assessment consisting of 2 coding questions and some ML-based MCQs. The candidate solved 1 medium coding question and partially solved the second. The MCQs were attempted but not fully completed due to time constraints.
A 30-minute HR round where the HR discussed the candidate's application and expectations. The candidate rejected the offer after being selected for the next round.
The candidate cleared the online assessment and two technical rounds, ultimately receiving a job offer with a package of 21 LPA.
Online Assessment on HackerRank consisting of 30 MCQs on React, Redux, JavaScript, HTML, and CSS. Additionally, one problem-solving question was asked, which involved solving a problem using the Hashing algorithm. The candidate solved 8 out of 10 test cases and performed averagely on the MCQs.
Focused on Data Structures and Algorithms (DSA) with a problem on Stack & Queue (Valid Parenthesis), React Native architecture concepts (Turbo Modules & Fabric Architecture), JavaScript-related questions (SetTimeOut output-based), a problem to count string words, and a question on parallel API execution using Promises.
Valid Parenthesis problem (Stack & Queue)
React Native Architecture (Turbo Modules & Fabric Architecture)
SetTimeOut output-based question
Count String words problem
Promise API call for parallel execution of multiple APIs
Covered React Native design patterns, React Native architecture, Timer-related problems in React Native, components for API calls, and Native Modules (Bridge Concept).
Design patterns used in React Native
React Native Architecture
Timer Problem in React Native
Component for calling the API
Native Modules (Bridge Concept)
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