Interview questions for Tiger Analytics Analytics Consulting
Hi everyone, this topic is for sharing Preparation guidelines and interview experience for Tiger Analytics Analytics Consulting
The Analytics Consulting role at Tiger Analytics involves a multi-stage assessment and interview process, designed to evaluate both technical skills and business proficiency. Below is a summary of the process and key points from the interviews you provided:
Assessment Test Rounds:
Resume Shortlist Screening based on relevance and clarity of skills/experience listed on the resume.
Coding Test (MCQ-Based) Multiple-choice questions on programming and coding concepts.
Coding Test (Pairing Round) Collaborative problem solving with another candidate; assessed on communication and coding approach.
SQL & Python Coding Test (Real-time) Hands-on problems involving SQL queries (joins, subqueries) and Python data manipulation (Pandas) under time constraints.
Interview Rounds:
Technical Round 1 Data science fundamentals and concepts aligned with the candidate’s resume.
Technical Round 2 Deeper data science and analytics problem-solving; follow-ups based on prior answers and projects.
Project-Specific Discussion End-to-end deep dive into past projects: methodology, challenges, tools, and business impact.
Team Fit & Career Plan (with Senior VP) Focus on collaboration style, career aspirations, and alignment to company goals.
HR Round Covers salary expectations, relocation, availability, and general fit; may be recorded.
Interview Preparation Tips:
Be thorough with every detail on your resume; be prepared to discuss your projects end-to-end.
Brush up on core data science concepts; be ready to explain algorithms and their use cases.
Practice SQL joins and subqueries, and Pandas data manipulation under time pressure (e.g., on LeetCode and HackerRank).
For pairing rounds, practice collaborative coding and verbalizing your approach clearly.
Research company culture and values; articulate how your goals align with the role.
Dress professionally even for virtual interviews, especially for HR rounds that may be recorded.
Technical / Analytics / Data Science
Explain different unsupervised algorithms.
Compare K-means and hierarchical clustering—when would you use each and why?
How do you choose the number of clusters (e.g., elbow method, silhouette score)?
What is PCA, when would you use it, and how do you interpret the components?
How do you evaluate the quality of a clustering solution?
How would you handle high-dimensional data before modeling?
SQL & Python (Hands-on Coding)
Write an SQL query using multiple joins to combine tables and compute aggregates.
Use a subquery to filter rows based on aggregated criteria.
Write an SQL query to find the top-N items per group.
Using Python (Pandas), read a dataset and perform data cleaning (handle missing values, type conversion).
Merge/join DataFrames and compute derived columns in Pandas.
Group, aggregate, and sort data in Pandas to answer a business question.
Implement a Python function to parse and transform raw data efficiently.
Programming Concepts (MCQ-Based)
Identify the output of a given code snippet.
Choose the most appropriate data structure for a given scenario.
Determine the time complexity of a code fragment.
Answer questions on Python fundamentals (e.g., mutability, lists vs. tuples, dictionary behavior).
Project / Case Discussion
Walk us through your most impactful analytics project end-to-end.
What were the data sources, quality issues, and how did you address them?
What methodology did you follow and why?
Which algorithms did you try, and why did you select the final approach?
What challenges did you face and how did you resolve them?
How did you measure success and business impact? Which metrics did you track?
What trade-offs did you make (e.g., accuracy vs. interpretability, speed vs. complexity)?
How did you ensure stakeholder buy-in and communicate results to non-technical audiences?
What was your individual contribution versus the team’s?
If you had more time, how would you improve or extend the project?
HR / Personality / Behavioral
Tell me about a time you collaborated across functions to deliver a result.
How do you handle disagreements or conflicts in a team, including during pair programming?
Describe a situation where you had to explain complex analytics to a non-technical stakeholder.
What are your career aspirations for the next 2–3 years, and how does this role align with them?
Why Tiger Analytics and why Analytics Consulting?
How do you prioritize tasks under tight deadlines?
HR / Logistics
What are your salary expectations?
Are you open to relocation?
What is your availability or notice period to join?
If you have attended the process from your campus, pls share your experiences here; Please follow guidelines
Application Process: I was interviewed in April 2023. The process involved three rounds: Resume Shortlist, Technical Round 1, and Technical Round 2.
Interview Rounds:
Round 1 - Resume Shortlist:
Questions Asked: None (resume screening).
Your Approach: Ensured my resume was crisp and highlighted relevant skills and experiences.
Outcome: Passed to the next round.
Round 2 - Technical Round 1:
Questions Asked: Questions related to data science.
Your Approach: Answered based on my knowledge and experience in data science.
Outcome: Advanced to the next round.
Round 3 - Technical Round 2:
Questions Asked: Further questions related to data science.
Your Approach: Provided detailed answers and examples from my projects.
Outcome: Awaiting results.
Preparation Tips:
Be thorough with everything mentioned in your resume.
Brush up on data science concepts and be ready to discuss your projects in detail.
Conclusion:
The interview process was smooth, and the questions were aligned with my resume. I focused on presenting my skills clearly and concisely. For future candidates, I’d recommend being well-prepared to discuss any aspect of your resume and having a strong grasp of data science fundamentals.
Application Process: Applied through campus placement.
Interview Rounds:
Round 1 - One-on-one Technical Interview:
Questions Asked:
Questions on MLP (Multi-Layer Perceptron) and Machine Learning concepts.
Your Approach:
I focused on explaining the fundamentals of MLP, its architecture, and its applications in real-world scenarios. For the Machine Learning questions, I discussed key algorithms and their use cases.
Outcome:
The interviewer seemed satisfied with my explanations, and I advanced to the next stage.
Preparation Tips:
Brush up on core Machine Learning concepts, especially neural networks and deep learning.
Practice explaining technical concepts clearly and concisely.
Review real-world applications of ML algorithms to demonstrate practical knowledge.
Conclusion:
The interview was a great learning experience. I felt confident in my technical knowledge, but I could have prepared more case studies to showcase my problem-solving skills. For future candidates, I’d recommend focusing on both theoretical and practical aspects of Machine Learning.
Advanced concepts related to projects on the CV, including mathematical aspects.
Your Approach: Honest about areas of uncertainty while demonstrating depth in familiar topics.
Outcome: Successful completion of the interview process.
Preparation Tips:
Prepare Python and ML fundamentals thoroughly.
Be honest during the interview; it’s okay to skip questions you’re unsure about.
Conclusion:
The interview process was comprehensive, covering both theoretical and practical aspects of analytics. Being well-prepared and honest helped me navigate the rounds effectively. My advice to future candidates is to focus on fundamentals and be transparent about their knowledge gaps.
Application Process: I applied through campus placement.
Interview Rounds:
Round 1 - Coding Test Round:
Questions Asked: The round consisted of a coding test followed by two interviews—one technical and one focused on statistics.
Your Approach: I brushed up on my coding skills, especially in Python, and revised key statistical concepts to prepare for the interviews.
Outcome: I cleared this round and moved on to the next stage.
Round 2 - Case Study Round:
Questions Asked: This round involved basic guesstimates and case studies.
Your Approach: I practiced solving case studies and guesstimates beforehand to improve my analytical thinking and problem-solving skills.
Outcome: The round went well, and I felt confident about my performance.
Preparation Tips:
Study hard and focus on both technical skills (like Python and statistics) and analytical problem-solving.
Practice case studies and guesstimates to get comfortable with real-world scenarios.
Conclusion:
Overall, the interview process was smooth, and I felt well-prepared. I would advise future candidates to focus on both coding and analytical skills, as the rounds test a mix of these. Practicing case studies and guesstimates can give you an edge in the later stages.
Location: Dehradun Institute of Technology, Dehradun
Application Process: Applied via campus placement at Dehradun Institute of Technology, Dehradun, before October 2023.
Interview Rounds:
Round 1 - Coding Test (MCQ-Based):
Questions Asked: Multiple-choice questions focused on coding concepts.
Your Approach: Prepared by revising core programming concepts and practicing MCQ-based coding questions.
Outcome: Cleared the round successfully.
Round 2 - Coding Test (Pairing Round):
Questions Asked: A collaborative coding round where pairing with another candidate was required.
Your Approach: Focused on clear communication and problem-solving while working with the partner.
Outcome: Advanced to the next round.
Round 3 - Technical Round:
Questions Asked:
Explain different unsupervised algorithms.
Your Approach: Discussed key unsupervised algorithms like K-means clustering, hierarchical clustering, and PCA, along with their use cases.
Outcome: Provided a detailed explanation and cleared the round.
Preparation Tips:
Revise core programming concepts for MCQ-based rounds.
Practice collaborative coding to improve pairing skills.
Understand and be able to explain unsupervised algorithms clearly.
Conclusion:
The interview process was structured and tested both technical and collaborative skills. Preparing for MCQ-based questions and practicing pairing coding helped a lot. For future candidates, focus on clarity in explanations and teamwork during pairing rounds.
Application Process: I applied through a recruitment consultant and was interviewed in July 2024.
Interview Rounds:
Round 1 - Coding Test:
Questions Asked:
Describe your project.
Explain a machine learning concept.
Your Approach: I briefly summarized my project, focusing on the problem statement, methodology, and outcomes. For the machine learning concept, I chose to explain supervised learning and provided a real-world example.
Outcome: Passed this round.
Round 2 - Case Study:
Questions Asked: Presentation-based question on a case study.
Your Approach: I structured my presentation to clearly define the problem, analyze the data, propose solutions, and justify my recommendations. I also prepared for potential follow-up questions.
Outcome: Successfully cleared this round.
Round 3 - HR Round:
Questions Asked:
Tell something which you haven’t put in your resume.
Your Approach: I shared a personal hobby or interest that wasn’t mentioned in my resume, highlighting how it reflects my personality and skills.
Outcome: Cleared the HR round.
Preparation Tips:
Brush up on fundamental machine learning concepts and be ready to explain them clearly.
Practice structuring case study presentations logically and concisely.
Prepare for HR questions by reflecting on your experiences and personality traits not covered in your resume.
Conclusion:
Overall, the interview process was smooth and well-structured. I felt confident in my technical rounds but realized the importance of being concise in my answers. For future candidates, I’d recommend practicing mock case studies and being prepared to discuss both technical and non-technical aspects of your profile.
Application Process: I applied for this role through LinkedIn in July 2024.
Interview Rounds:
Round 1 - One-on-one Round:
Questions Asked: They asked about my experience, explained the job description, and assessed my suitability for the role.
Your Approach: I highlighted my relevant experience and how it aligns with the job requirements.
Outcome: I passed this round.
Round 2 - Technical Round:
Questions Asked: Detailed questions about my experience with Google Analytics, Adobe Analytics, SQL, Tableau, and Python.
Your Approach: I provided specific examples of my work and projects involving these tools.
Outcome: I advanced to the next round.
Round 3 - Group Discussion Round:
Questions Asked: It was a discussion with the client to evaluate my fit for the role.
Your Approach: I actively participated, shared insights, and demonstrated my problem-solving skills.
Outcome: Successfully cleared this round.
Round 4 - HR Round:
Questions Asked: Salary expectations and availability to join.
Your Approach: I was transparent about my expectations and flexibility.
Outcome: Final discussions were held, and I awaited further updates.
Preparation Tips:
Brush up on tools like Google Analytics, Adobe Analytics, SQL, Tableau, and Python.
Be ready to discuss your past projects and how they align with the job role.
Practice group discussions to improve communication and collaboration skills.
Conclusion:
Overall, the interview process was thorough and focused on assessing both technical and interpersonal skills. I felt well-prepared, but I could have researched more about the client’s specific needs for the Group Discussion round. My advice to future candidates is to thoroughly understand the job description and be ready to showcase both technical expertise and teamwork.
Application Process: Approached by the company and interviewed in January 2024.
Interview Rounds:
Round 1 - Coding Test:
Questions Asked:
SQL: Joins and a cumulative sum question.
Python: Function definitions to lambda functions, such as squaring the odd digits in an array using a lambda function.
Data Science: Which test metric to use for assessing CTR (Click-Through Rate) and when to differentiate between Mean Squared Error and R2 coefficient of variation.
Your Approach: Focused on refreshing SQL joins and Python lambda functions beforehand. For the Data Science questions, I relied on understanding the context of the problem to choose the appropriate metric.
Outcome: The questions were of medium difficulty, and I felt prepared for most of them.
Preparation Tips:
Topics to prepare:
SQL: Rolling averages and cumulative sums.
Python: Lambda functions and basic operations.
Data Science: Statistical tests and evaluation metrics.
Business case studies.
Refresh your basics in Python and SQL, as they are tested at a medium level. Data Science questions will challenge your problem-solving skills.
Conclusion:
The interview was a good mix of technical and practical questions. I felt confident in my SQL and Python skills but realized I could have brushed up more on Data Science evaluation metrics. My advice to future candidates is to practice real-world problem-solving scenarios and ensure a strong grasp of the fundamentals.
Application Process: The application process involved two interview rounds: a coding test and an HR round.
Interview Rounds:
Round 1 - Coding Test:
Questions Asked: The coding test included SQL and Python questions designed to test basic logic and problem-solving skills.
Your Approach: I reviewed fundamental SQL queries and Python functions beforehand to ensure I was comfortable with the basics. During the test, I took my time to understand each question before writing the code.
Outcome: I passed this round and moved on to the HR round.
Round 2 - HR Round:
Questions Asked:
A Python coding question involving functions.
An SQL question requiring the use of a CASE statement.
Your Approach: For the Python question, I ensured my function was efficient and handled edge cases. For the SQL question, I structured my query logically to incorporate the CASE statement effectively.
Outcome: The HR round went well, and I received positive feedback on my approach.
Preparation Tips:
Brush up on basic SQL queries and Python functions.
Practice writing clean and efficient code.
Familiarize yourself with common HR interview questions to articulate your thoughts clearly.
Conclusion:
Overall, the interview process was smooth and well-structured. I felt prepared for both rounds, but I could have practiced more complex SQL scenarios to feel even more confident. My advice to future candidates is to focus on the fundamentals and stay calm during the interview.
Application Process: Applied through an online job portal. The process involved four rounds of virtual interviews.
Interview Rounds:
Round 1 - SQL & Python Coding Test:
Questions Asked: Real-time coding questions focused on SQL queries and Python programming.
Your Approach: Brushed up on SQL joins, subqueries, and Python data manipulation libraries like Pandas. Practiced coding under time constraints to simulate the test environment.
Outcome: Cleared the round by solving all problems efficiently.
Round 2 - Project-Specific Discussion:
Questions Asked: Deep dive into past projects, focusing on methodologies, challenges faced, and outcomes.
Your Approach: Prepared a detailed walkthrough of each project, ensuring clarity on every aspect, including data sources, tools used, and business impact.
Outcome: Successfully communicated project details and passed the round.
Round 3 - Team Fit & Career Plan with Senior VP:
Questions Asked: Questions about team collaboration, career aspirations, and alignment with the company’s goals.
Your Approach: Researched the company’s culture and values beforehand. Articulated how my skills and career goals align with the role.
Outcome: Positive feedback on cultural fit and moved to the next round.
Round 4 - HR Round:
Questions Asked: General HR questions about salary expectations, relocation, and availability.
Your Approach: Dressed professionally as the round might be recorded. Answered honestly and confidently.
Outcome: Final confirmation and offer discussion.
Preparation Tips:
SQL & Python: Focus on real-time coding practice. Websites like LeetCode and HackerRank are great for this.
Projects: Be prepared to explain every detail of your projects, including the “why” behind each decision.
Behavioral: Research the company’s values and align your answers accordingly.
Dress Code: Even for virtual interviews, dress professionally, especially for HR rounds.
Conclusion:
Overall, the interview process was thorough but well-structured. The key to success was thorough preparation, especially in coding and project discussions. For future candidates, I’d recommend practicing coding under time pressure and being very clear about your project contributions. Also, don’t underestimate the importance of the HR round—it’s your final impression!