Ebas Group Product Manager Interview: Questions, Experience & Prep (2026)

Ebas Group currently has 11 open Product Manager roles, making it one of the more active PM hirers to target right now. As of mid-2026, knok jobradar tracks 2009 PM openings across India, with Bangalore leading at 271 open roles, followed by Delhi (177) and Mumbai (56).

Candidates report that Ebas Group's PM interview process typically spans three to five rounds, covering product sense, analytical thinking, stakeholder communication, and past experience. The rounds are described as structured but conversational, with interviewers placing more weight on real-world problem-solving than on framework recitation.

Salary bands for PM roles in India currently run from 12-20 LPA at the Associate PM level to 55-90+ LPA for Group or Principal PMs, based on knok jobradar data. Your offer from Ebas Group will typically fall within these bands depending on your seniority and the specific business unit.

Product Sense and Strategy

1. Walk me through a product you use every day. What one thing would you change about it, and why?
2. Ebas Group works across multiple business verticals. How would you prioritise features for the next quarter if you had to choose between improving retention and acquiring new users?
3. A key product metric has dropped noticeably week-on-week. Walk me through how you would diagnose the problem.

Execution and Prioritisation

1. How do you decide what goes into the next sprint when engineering bandwidth is tight?
2. You have requests coming in from sales, support, and your own user research. How do you prioritise across all three?
3. Tell me about a time you shipped something that did not perform as expected. What did you do next?

Stakeholder and Cross-functional Work

1. Describe a situation where engineering and business stakeholders disagreed on scope. How did you resolve it?
2. How do you present product decisions to non-technical senior leadership?
3. Give an example of influencing an important decision without having direct authority.

Analytics and Growth

1. What metrics would you track for a newly launched B2B SaaS feature?
2. How would you design an A/B test for a change to the checkout or onboarding flow?
3. If you could only track one north-star metric for a product you have worked on, what would it be and why?

Q: Tell me about a product you improved that had a measurable impact.

*Situation:* At my previous company, mobile onboarding completion was low, which was dragging down trial-to-paid conversion.

*Task:* I was asked to own the onboarding redesign from discovery through to launch.

*Action:* I ran user interviews to find the exact drop-off points, identified the top friction areas, worked with design on a simplified three-step flow, and coordinated with engineering to ship it within the quarter.

*Result:* Onboarding completion improved meaningfully, and the team reported a clear lift in trial-to-paid conversion within the first month. The work was cited in the quarterly business review as a key growth driver.

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Q: Describe a time you had to push back on a stakeholder request.

*Situation:* A senior sales lead wanted a highly custom report built for a single enterprise client before our quarterly roadmap review.

*Task:* I needed to protect the team's capacity while keeping the relationship with the sales lead intact.

*Action:* I acknowledged the business value of retaining the client, then walked the sales lead through our current sprint commitments and the opportunity cost of context-switching. I proposed a lightweight workaround using existing data exports, deliverable within days, and committed to putting the full feature on the next quarter's roadmap.

*Result:* The stakeholder accepted the workaround, the client stayed happy, and the team shipped on schedule. The feature was eventually built and rolled out to multiple clients.

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Q: How have you used data to change a product decision?

*Situation:* Our team assumed users wanted more filtering options in our search feature, based on vocal internal feedback.

*Task:* Before committing engineering time to the work, I wanted to validate whether the assumption held up.

*Action:* I pulled session recordings, ran a short in-app survey, and reviewed support tickets. The data showed very few users ever engaged with the existing filters, and the real pain point was slow search results, not missing filter types.

*Result:* We redirected the engineering effort to search performance. In-app satisfaction scores improved in the following quarter, and the decision freed up significant engineering capacity for higher-priority work.

STAR (Situation, Task, Action, Result) is the baseline for all experience questions. Keep each part tight: one or two sentences on context, one on your specific role, two or three on what you actually did, and one clear result.

Product-sense or design frameworks (such as a simplified CIRCLES approach) work well for 'design a product' questions. Cover: clarify the goal, identify the user, list their needs, prioritise ruthlessly, outline the solution, then name the trade-offs.

Root-cause or funnel analysis is expected for metric-drop questions. Start at the top of the funnel, rule out data collection errors first, then segment by platform, cohort, or geography before committing to a hypothesis.

Prioritisation frameworks such as RICE (Reach, Impact, Confidence, Effort) or a simple impact-vs-effort grid are useful for 'how do you prioritise' questions. Candidates report that interviewers respond well when you name a framework and then adapt it to the business context, rather than applying it like a checklist.

Communication framing for stakeholder questions: structure your answer around 'what happened, why it mattered, and what you did next.' This signals clarity of thinking and executive-ready communication.

Based on candidate reports, Ebas Group PM interviewers are typically looking for four qualities.

Ownership mindset. They want to see that you treat the product as your own business, not just a delivery task. Use language like 'I decided' and 'I pushed for' rather than 'the team decided.'

Structured thinking under ambiguity. Interviews often involve open-ended scenarios with no single correct answer. Interviewers are reportedly more interested in how you break a problem down than in whether you land on their exact answer.

Stakeholder empathy. Ebas Group works across business units, so candidates who demonstrate comfort navigating sales, engineering, and leadership simultaneously tend to stand out.

Data-informed, not data-paralysed. Interviewers want you to cite data when it supports a decision, but they also want to see that you can move forward when data is incomplete. Treating every question with 'I would wait for full statistical significance before deciding' can read as indecisive at the PM level.

Week 1: Know the company and the role.
Read Ebas Group's publicly available product announcements and news. Identify whether their products are B2B, B2C, or platform-oriented, and note the industries they serve. Map two or three stories from your past experience directly to their context before your first conversation.

Week 1-2: Sharpen your story bank.
Prepare five to six strong STAR stories covering: a product you owned end-to-end, a conflict you resolved, a data-driven decision you made, a failure and what you learned, and a time you influenced without authority. Practise these out loud, not just in your notes.

Week 2: Practise product-sense questions.
Pick two or three products you know well and rehearse a crisp 'what I would change and why' answer for each. Run at least two timed mock interviews with a peer or mentor.

Ongoing: Brush up on analytics basics.
Be ready to walk through funnel analysis, A/B testing logic, and metric selection. You do not need to be a data analyst, but you should be comfortable explaining your reasoning step by step without hesitation.

Before the interview: Prepare sharp questions.
Ask about the team's current biggest product challenge, how success is measured for this role in the first six months, and how PMs collaborate with engineering. These signal genuine interest and strategic thinking.

Jumping to a solution before understanding the problem. This is the most commonly reported reason candidates stumble in product-sense rounds. Always clarify goals and user context before proposing anything.

Vague results in STAR answers. Saying 'the product improved' is weaker than saying 'retention improved and the team shipped on schedule.' Even qualitative outcomes, stated with confidence, are better than no outcome at all.

Overusing jargon without substance. Words like 'synergy,' 'leverage,' or 'move the needle' without concrete meaning behind them signal shallow thinking. Speak plainly about what you actually did and why.

Not acknowledging trade-offs. Every product decision has a cost. Candidates who present only the upside of their choices come across as inexperienced. Name the trade-off you accepted and explain why it was worth it.

Treating every question as a framework exercise. Frameworks are a starting point, not a script. Mid-to-senior level interviewers typically expect you to adapt any framework to the specific context, not recite it verbatim.

Skipping clarifying questions. In product-sense and case questions, candidates report that interviewers actively reward those who pause to clarify scope before diving in. It mirrors what a strong PM does on the job every day.