Job Title: Senior Product Analyst

Turn Product Usage Into Product Strategy

MediaMint is transitioning from best-in-class RevOps services for global leaders (Netflix, Pinterest, Disney, and more) into an AI-first organization. At the center of that shift is a self-serve, AI-native platform of modular AI agents that lets users build, deploy, test, and operate intelligent agents and automated workflows compressing hours of advertising, marketing, and sales-ops work into minutes.

We're looking for a Senior Product Analyst to be the first dedicated analytics owner for the platform. You'll define how we measure an agentic product where none of the conventions exist yet and move Product from anecdote-driven decisions to evidence-led strategy.

About the Role

This is a hands-on, high-ownership role for someone who wants to build a product-analytics practice, not inherit one. You'll define the metric system for an AI-native platform, design the instrumentation behind it with engineering, and turn raw usage into the questions and answers that shape the roadmap.

Because the core action on our platform an agent execution is non-deterministic, this goes beyond classic SaaS analytics. You won't just measure whether a feature was used; you'll measure whether it worked: task success, quality, human intervention, and the cost of getting there. You'll be the person who can walk into a prioritization discussion and change what gets built, improved, scaled, or killed with data.

You'll answer questions like:

- Which product workflows are actually driving adoption?
- Where do users drop off during agent creation or execution?
- Which agents, features, or workflows create measurable value?
- What signals indicate retention, expansion, or churn risk at the user and account level?
- How are new features performing after launch?
- How should feedback from agent usage feed the roadmap?

What You'll Do

1. Define the Measurement System

- Build the core metric tree from scratch North Star plus the input metrics that move it for an agentic, self-serve platform.
- Define activation, adoption, retention, engagement, and expansion metrics that hold up to scrutiny and that PMs actually trust.
- Establish account/team-level adoption rollups for a B2B, product-led motion.

2. Own Instrumentation Quality

- Partner with engineering to design event taxonomies and tracking plans for the full agent lifecycle creation, configuration, testing, deployment, and execution.
- Audit and close instrumentation gaps so downstream analysis is trustworthy by default.

3.



Analyze Funnels and Friction

- Map the agent-creation and agent-execution funnels and pinpoint where users stall, abandon, or fail.
- Turn friction points into prioritized, quantified opportunities for Product and Design.

4. Measure Value and Outcomes

- Connect product usage to business outcomes — value delivered, cost incurred, and unit economics per agent/execution.
- Identify which agents, features, and workflows create measurable value, and which quietly don't.

5. Measure Agent Quality and Feedback Loops

- Define proxies for task success, output quality, and human-in-the-loop review/override rates.
- Build the feedback-loop measurement that tells Product and Engineering where agent quality is strong, weak, or degrading — and feed it back into the roadmap.

6. Retention, Expansion, and Risk Signals

- Define leading indicators of retention, product-led expansion, and churn risk.
- Surface these signals to Product, Customer Success, and Sales/Solutions so they can act early.

7. Influence the Roadmap

- Translate ambiguous product questions into crisp, measurable analysis.
- Deliver recommendations — with clear evidence — on what to build, improve, scale, or deprioritize, and make analytics a standing input to prioritization.

8. Partner Across Functions

- Work closely with Product, Engineering, Design, Customer Success, Sales/Solutions, and Implementation to make sure the right questions get asked and the answers get used.

Must-Have Skills

- 3–7 years in product analytics for a B2B SaaS or platform product — ideally with a self-serve / product-led growth motion.
- Confident, independent SQL — you write complex queries (cohorting, funnels, window functions) without hand-holding.
- Hands-on ownership of a product analytics platform — Amplitude, Mixpanel, PostHog, Heap, or equivalent.
- Demonstrated track record defining product metrics from scratch — activation, retention, engagement, funnels — not just consuming existing dashboards.
- Experience designing instrumentation and event schemas in partnership with engineering.




- Ability to translate ambiguous product questions into structured analysis and transparent recommendations.
- Evidence of influencing roadmap or product decisions with data.
- Strong product judgment and the communication skills to make analysis land with senior stakeholders.

Good-to-Have / Bonus Skills

- Experience with AI-native, agentic, LLM, copilot, or workflow-automation products — a strong differentiator, not a requirement.
- Experience with builder / creation products or developer / no-code / low-code platforms, where measuring how people build things matters.
- Experience measuring AI output quality, task success, evaluation, or human-in-the-loop review.
- Unit-economics / cost-per-usage analysis for consumption-priced products.
- Data modeling (dbt), warehouse experience (Snowflake / BigQuery / Redshift), BI tools (Looker / Metabase / Tableau).
- Python / pandas for deeper analysis.

What Success Looks Like

By 90 days

- A core metric tree exists and is adopted — the team agrees on what "adoption," "activation," and "value" mean for the platform.
- Instrumentation gaps are audited and the first trustworthy adoption/activation dashboards are live.

By 6 months

- The agent-creation and agent-execution funnels are mapped, with the top drop-off points quantified and handed to Product and Design.
- At least one analysis has measurably changed a roadmap decision.

By 12 months

- Retention, expansion, and churn-risk signals are tracked at user and account level and used by Product, CS, and Sales.
- An agent-quality and feedback-loop measurement framework is in place.
- Analytics is a permanent, expected input into roadmap prioritization — the default is now evidence, not anecdote.

Why This Role Matters

You will be the first person to define how an AI-native platform understands itself. In a product where the core action is non-deterministic and the measurement conventions don't exist yet, the person who defines the metrics defines the strategy.

- You set the standard. The metric tree, the funnels, the definition of a "good" agent run — you build them, and the whole product team reasons in your terms.
- You shape the roadmap. Your analysis decides what gets built, improved, scaled, or cut across a growing base of global clients.
- You build the practice. This is a foundational hire with a clear path to Product Analytics Lead or broader Product / Growth Analytics ownership as the function grows.

Ready to decide what a product should build with evidence instead of instinct? Apply now.

📌 Senior Product Analyst (Telangana)
🏢 MediaMint
📍 Telangana