Data, AI & SAP Job Interview Preparation

Data, AI & enterprise-platform job interviews

Realistic hiring loops for data analysts, data and BI engineers, product analysts, ML and GenAI engineers, applied data scientists, and SAP, Salesforce, ServiceNow and Power Platform specialists. Technical rounds are scenario-based and probe trade-offs, not definitions.

Any employer hiring for data, analytics, ML or enterprise-platform roles — product companies, IT services and consulting firms (TCS, Infosys, Accenture, Deloitte, Capgemini), GCCs of global banks and retailers, fintechs and startups 3 roles 2 languages 5 AI interviewers

From our research

What this interview looks like

Data, AI and enterprise-platform hiring in India typically runs an HR or recruiter screen, a hiring-manager round on past projects, one or two technical rounds (a live SQL or case discussion for analytics, a modelling and systems discussion for ML, a configuration and scenario walkthrough for SAP, Salesforce, ServiceNow and Dynamics), and a behavioural or fitment round, spread over one to three weeks. Many IT-services and GCC loops add a client interview before the offer. ExamPilot compresses this into one 17-minute panel: HR screen, hiring-manager round on what you actually built and the business result, a role-specific technical round led by a lead or principal, a STAR behavioural round and a closing on expectations. Rounds are pass/fail in real companies; here every round is scored on role knowledge, problem solving, communication and attitude, with specific feedback on the trade-offs you did and did not mention.

Data & analyticsML & GenAISAPSalesforceServiceNowExperiencedTechnical round

Choose your role

3 roles available

Each role has its own panel, rounds and domain list.

What to expect

5 rounds, one after another

A private-sector hiring loop compressed into one sitting, the way most Indian companies run it: HR screen → hiring-manager round on your actual work → role/technical round led by a lead → communication & behavioural (STAR) round → closing on expectations and your questions. Works for software, product, sales, finance, HR, support and campus hiring by swapping the role bank.

  1. Round 1

    HR screen

    · 3:00

    Understand background, motivation for this role and company, and basic logistics; check that the resume story holds together.

    Ms. Neha Kulkarni
    2–3 questionsup to 1 follow-up each
  2. Round 2

    Hiring-manager round

    · 4:00

    Assess ownership, judgement and how the candidate actually works: what they shipped, closed or ran, their specific part, and how they would approach this job.

    Ms. Priyanka Raghavan
    2–3 questionsup to 2 follow-ups each
  3. Round 3

    Role / technical round

    · 5:00

    Test role knowledge and problem-solving in the chosen specialisation: fundamentals, one scenario to think through aloud, and trade-offs.

    Ms. Ananya Sen
    3–4 questionsup to 2 follow-ups each
  4. Round 4

    Communication & behavioural round

    · 3:00

    Assess collaboration, conflict handling, resilience and clarity of expression through Situation–Task–Action–Result examples.

    Ms. Priyanka Raghavan
    2–2 questionsup to 1 follow-up each
  5. Round 5

    Expectations & closing

    · 2:00

    Cover practical expectations and let the candidate ask questions; close professionally.

    Ms. Neha Kulkarni
    1–2 questions

Meet the panel

Your AI interviewers

Distinct personas, voices and questioning styles, briefed on this interview.

Ms. Neha Kulkarni

Chair

HR member

HR business partner, IT services

Friendly, efficient, detail-checking.

Female voice · 4 languages

Ms. Priyanka Raghavan

Hiring manager

Head of Data & Analytics, consumer internet company (hiring manager for data, analytics and ML teams)

Sharp, numbers-first, allergic to dashboards nobody acts on.

Female voice · 3 languages

Ms. Ananya Sen

Technical lead

Staff engineer / technical lead

Curious, precise, collaborative.

Female voice · 3 languages

Mr. Arjun Nair

Technical lead

Principal Machine Learning Engineer, AI platform team (technical interviewer for ML, data science and GenAI roles)

Calm, rigorous, enjoys a good trade-off discussion more than a right answer.

Male voice · 3 languages

Mr. Vikram Malhotra

Hiring manager

Engineering manager / Regional manager (hiring manager)

Direct, outcome-focused, fair but demanding.

Male voice · 2 languages

Languages

Answer in the language you think in

Interviewers speak and understand each language natively. Switch mid-answer if you like.

  • English
  • Hinglish (Hindi + English)Hinglish

How scoring works

A report you can act on

  1. Every round is scored on the rubric

    Each interviewer scores only the criteria their round covers. Weights add up to your overall score out of 100.

  2. Only what you actually said counts

    Feedback quotes your own answers. Rounds you skip show as “Not assessed” rather than a zero.

  3. Communication is always measured

    Fluency, clarity, confidence and structure are tracked across the whole interview, in any language.

  4. Pass mark, then a plan

    You see the pass mark for this interview, your gaps, and the courses that close them fastest.

Scored on

  • Role knowledge30%

    Depth and accuracy of role/domain knowledge; understands the tools, concepts and trade-offs of the job; grounds claims in real work.

  • Problem solving & structure25%

    Breaks problems down, asks clarifying questions, reasons about trade-offs and edge cases, structures answers (context, action, result).

  • Communication25%

    Clear, concise, confident; listens and answers the question asked; adapts the explanation to the listener; professional register.

  • Culture & attitude20%

    Ownership, honesty about limits, coachability, collaboration, motivation for this role and realistic expectations.

Pass mark 65 / 100 · Corporate interview — HR, hiring manager & role round (panel)

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