How Tiger Analytics hiring actually runs: consulting-style analytics cases with coding screens that vary by role. Synthesized from recent candidate reports with rounds reorganized and prep notes added - treat this as a prep map, not a script.
Round-wise process at a glance
Round
Typical format
What clears you
Written OA
Aptitude + tech MCQs
Accuracy under volume
Live coding
~1 hr Meet + jam board
Clear approach
Analytics / case
Null handling, business scenarios
Structured thinking
Problem-solving (lateral)
Projects + coding + cases
Communication
HR
Why Tiger, strengths
Fit
Technical interview experience 1 - Data Analyst On-Campus (selected)
Tell me about yourself (network hiccup → backup interviewer)
How to fill null/missing values beyond mean/mode/median with scenarios
Sales prediction: product sold at 20 vs 10 elsewhere - relate fields and predict sales
Feedback was middling (“OK”) yet advanced
Round 4 - HR
Routine HR questions
Final result: Selected (4 Intern+FTE offers next morning)
Takeaways
What decided it: Surviving a large aptitude+MCQ funnel, clear jam-board coding communication, then structured null-handling and sales-prediction reasoning even when feedback sounded middling.
Common fail: Only mean/mode/median for missing values; silent coding on Meet.
How to practise: List three null-imputation strategies with business scenarios; narrate approach on a jam board for 60 minutes.
What decided it: Multi-approach coding discussion, deep React/hooks answers matching the resume, then stock buy/sell optimised after stating brute force, plus clear Why Tiger values.
Common fail: Claiming React depth you cannot defend; jumping to optimal stock code without the O(n²) baseline.
How to practise: One hooks/closure sheet tied to your projects; brute→optimal narration on a classic array problem.
HR interview experience
HR interview experience
Candidate profile: Selected analyst / senior
HR themes
Why Tiger Analytics; strengths/weaknesses; values
Client-service mindset / collaboration
Final result: Offer when cleared
Key takeaways: Consulting culture language (client impact, experimentation) helps.
Pattern (candidate-reported, indicative): in-person assessment in Chennai / Bengaluru / Hyderabad with city filters on some posts; Python + SQL + C aptitude; large batch sizes in single-drive notes (~150 candidates in one public account).
Typical elimination themes
OA volume and accuracy under time.
Live SQL without IDE comfort; explain joins aloud.
HR enforces relocate + immediate joiner preference on some off-campus listings.
Outcome: varies by batch - treat as logistics + SQL prep signal, not a fixed question bank.
Dense prep checklist (Tiger Analyst / Trainee)
Stage
What to rehearse
Common failure mode
OA
SQL + Python + aptitude/probability MCQs
Ignoring easy MCQs for hard coding ego
Tech 1
Joins, window functions, OOP if claimed
Cannot write SQL on paper
Tech 2 / case
Metric → data → analysis → caveat
Jumping to “deep learning” without KPI
HR
Chennai/HYD/BLR relocate sentence
Applying from a non-listed city hoping HR waives
Off-campus
In-person logistics, notice period
Long notice when drive wants immediate joiners
Sample answers for high-frequency questions
Technical
Q. Impute missing values without mean/median/mode? A. Use domain rules, model-based imputation, time-series fill, category-wise estimates, or missingness indicators - justify from mechanism (MCAR/MAR).
Q. Best time to buy/sell stock (one transaction)? A. Track min price so far; max profit = price - min. O(n). Clarify single vs multiple transactions variant.
Q. Explain useMemo vs useCallback (if React role)? A. useMemo memoises a value; useCallback memoises a function reference - both for referential stability / costly compute.
HR
Q. Why Tiger? A. Analytics consulting + AI delivery for Fortune clients; map your SQL/ML/engineering strength to client outcomes.
Common questions checklist
OA / coding
Aptitude; complexity MCQs; jam-board DSA
Analytics / tech
Missing data; business cases; SQL; React/DSA if claimed
HR
Why Tiger; values; strengths
Tips from successful candidates
Assessment tips
Round 1 volume is high - accuracy over ego problems
Interview tips
Practise jam-board coding aloud
Structure analytics answers as assumptions → method → metric
These links are research inputs we used to map analyst vs senior loops - not copied page mirrors. We reorganized reports into process tables, outcome notes, and prep takeaways for Tiger Analytics.
