The hiring process
| Round | Format | What's tested |
|---|---|---|
| Online Assessment | ~90 min | Aptitude and data coding |
| Problem-Solving Round | ~45–60 min | Case studies and project depth |
| Business Understanding Round | ~30–45 min | Guesstimates and probability |
| HR Interview | ~20–30 min | Motivation, values, and fit |
Process and cutoffs vary by drive/team and change over time — confirm on the official careers page.
Round-by-round: exactly what's asked & how to prepare
Online Assessment
~90 minA test spanning data analysis, quantitative ability, logical reasoning, and verbal, often followed by a coding section of Python and SQL questions. It filters for both analytical reasoning and hands-on data skills.
- Quantitative ability and data interpretation
- Logical reasoning and verbal ability
- SQL queries: joins, aggregation, filtering, window functions
- Python data manipulation with pandas/NumPy
- Reading and reasoning about tabular data
- Practice intermediate SQL including joins, group-by, and window functions on free tiers like StrataScratch
- Drill pandas and NumPy data-manipulation problems
- Revise quantitative aptitude and data-interpretation sets
- Time yourself to handle the mixed section pacing
- Weak SQL beyond simple SELECTs
- Struggling with pandas syntax under time pressure
- Neglecting the aptitude/DI portion for the coding one
- Correct, efficient SQL including window functions
- Fluent pandas/NumPy data manipulation
- Balanced performance across aptitude and coding
Problem-Solving Round
~45–60 minAn elimination round with case-study questions (market entry, profitability, and similar) plus deep questions on your projects. Interviewers value a structured approach and clear assumptions over a memorized framework or a lucky final number.
- Structuring a market-entry or profitability case
- Breaking a business problem into a logical framework
- Deep dive into an analytics or ML project you built
- Interpreting data and drawing defensible conclusions
- Stating assumptions and reasoning transparently
- Practice case frameworks (profitability, market entry) from free case guides
- Prepare two projects you can discuss end to end with metrics
- Rehearse thinking aloud and stating assumptions clearly
- Practice interpreting a chart or dataset and forming a takeaway
- Jumping to an answer without structuring the problem
- Hiding assumptions instead of stating them
- Project stories with no measurable impact or your specific role
- A clear, logical structure applied to the case
- Explicit, reasonable assumptions
- Project depth with quantified impact
Business Understanding Round
~30–45 minAn elimination round centered on guesstimates and probability-based questions. The focus is squarely on the structure and reasoning of your approach, not the exact final figure. Communicate every step of your estimation.
- Guesstimates like market sizing or demand estimation
- Probability and expected-value reasoning
- Breaking estimates into logical components
- Sanity-checking assumptions and orders of magnitude
- Communicating quantitative reasoning clearly
- Practice 10–15 guesstimates using top-down and bottom-up structures
- Revise core probability: conditional probability, expected value, Bayes basics
- Rehearse narrating your assumptions and arithmetic aloud
- Practice quick sanity checks on your estimates
- Guessing a number without a visible breakdown
- Silent calculation instead of walking through logic
- Unreasonable assumptions you never sanity-check
- Structured, step-by-step estimation
- Sound probability reasoning
- Clear articulation of assumptions and checks
HR Interview
~20–30 minA standard behavioral close on strengths, weaknesses, values, and why Fractal. It confirms fit and communication after the demanding technical rounds.
- Why Fractal and interest in analytics/AI as a career
- Strengths, weaknesses, and personal values
- A teamwork or challenge situation
- Long-term goals in data and analytics
- Flexibility on location and role
- Read Fractal's focus on AI, analytics, and its client domains
- Prepare honest strengths, weaknesses, and a values example
- Have one clear STAR teamwork or challenge story
- Prepare a genuine reason for choosing analytics and Fractal
- Generic motivation not tied to analytics
- A weakness answer that sounds rehearsed or evasive
- No real awareness of what Fractal does
- Genuine passion for analytics/AI
- Self-aware, honest reflection
- Authentic alignment with the firm's work
What to master
- SQL (incl. window functions)
- Python (pandas/NumPy)
- Quantitative aptitude
- Case study frameworks
- Guesstimates
- Probability & statistics
- Data interpretation
- Business communication
Eligibility
Analytics/data-science roles typically seek strong quantitative aptitude with SQL/Python skills; exact branch and CGPA rules vary by drive.
Fractal Analytics salary & compensation (2026)
India-first analytics/AI services firm; solid but services-tier pay, not product-company level.
| Role / Level | Experience | India — total CTC | US — total comp | What to know |
|---|---|---|---|---|
| Data Analyst / Engineer | 0–2 yrs | ₹8–14 LPA | — | entry consultant; base-heavy, small variable bonus |
| Senior Consultant | 2–5 yrs | ₹16–26 LPA | — | client-facing delivery; bonus 10–15% |
| Principal Consultant | 5–8 yrs | ₹28–45 LPA | — | leads workstreams; some project-linked variable |
| Engagement/Client Partner | 8–12 yrs | ₹50–80 LPA | — | P&L/account ownership drives upside |
| Director / VP | 12+ yrs | ₹90 LPA–1.5 Cr | — | leadership; RSUs/carry-style long-term incentives |
How the package is structured
- Services model: base salary dominates, variable is modest (10–20%) vs product firms.
- Fastest growth is via consulting-track promotions and moving into client-partner/account roles.
- US/onsite deputation adds a large premium but is deployment-dependent, not standard.
- Switching to product companies (FAANG/fintech) is the common path to step-change comp.
Indicative 2026 market ranges aggregated from public sources (levels.fyi, Glassdoor, AmbitionBox, candidate reports). Compensation varies widely by location, team, level calibration, and negotiation — use these as directional benchmarks, not guarantees.
Your prep plan
- Days 1–3: master intermediate SQL including window functions and joins
- Days 4–5: drill pandas/NumPy data-manipulation problems
- Days 6–7: revise probability, statistics, and quantitative aptitude
- Days 8–10: practice 12+ guesstimates and 4+ business cases with structure
- Days 11–12: prepare two project deep-dives with quantified impact
- Days 13–14: mock interviews narrating reasoning plus HR prep
Want this plan dated to your interview?
Upload your resume and tell Whis your interview date — get a personalized, day-by-day plan built around your gaps, then run Whis live in the interview.
Get my personalized planFrequently asked questions
How much do case studies matter versus coding?
Both are gates. You need real SQL/Python plus structured case and guesstimate reasoning. Strong coding without business structure, or vice versa, usually fails an elimination round.
Do interviewers care about the exact guesstimate answer?
No. They weigh the structure, assumptions, and reasoning of your approach far more than the final number, so narrate every step.
Which is more important, SQL or Python?
Both feature heavily and the online test often splits questions between them. Be comfortable with intermediate SQL and pandas-based data manipulation.