This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sr. Product Analyst - AI Products based in India.
As a Senior Product Analyst, you’ll take ownership of measurement and performance analytics across a broad portfolio of AI-powered products. You’ll establish the metrics, reporting frameworks, and business reviews that guide product and commercial decisions. Working closely with product leadership and product managers, you’ll turn complex data into clear insights that influence the AI roadmap and strategic planning. You’ll work hands-on with SQL, cloud data warehouses, and business intelligence tools to build reliable reporting and answer high-impact questions. The role spans adoption, retention, cohort behaviour, product quality, experimentation, and AI usage economics. You’ll also help ensure that instrumentation and metric definitions are accurate and consistent across the product portfolio. This is an opportunity to combine deep analytical expertise with meaningful product ownership in a fast-moving AI environment.
Accountabilities:
- AI Portfolio Performance: Own the recurring business review for the AI product portfolio, developing the analysis and presenting performance insights to product leadership.
- Metric Ownership: Define, document, and maintain consistent metrics for each AI product, including activation, adoption, penetration, and north star metrics.
- Reporting & Dashboards: Build and maintain datasets, charts, and dashboards in Superset, while auditing existing reporting for hardcoded filters, stale queries, and inconsistent definitions.
- Data Analysis: Write advanced SQL queries against ClickHouse and Snowflake, including queries involving semi-structured JSON data, to answer business questions beyond standard dashboards.
- Business Planning: Track product performance against annual operating plans and re-forecasts, investigate variances, and explain the underlying product drivers.
- Retention & Adoption: Analyse sub-account-level retention, cohorts, and adoption funnels to identify where users disengage and understand the reasons behind behavioural patterns.
- AI Unit Economics: Analyse token consumption and model-level costs, translating usage into cost metrics and reporting cost-to-revenue ratios and margins by product.
- Product Quality Analytics: Measure AI product quality through evaluation results, containment and escalation rates, version comparisons, and appropriate sampling methodologies.
- Data Reconciliation: Investigate discrepancies between data sources and determine which source is most appropriate for specific analytical questions.
- Product Analytics: Partner with product managers to define instrumentation requirements before launches and analyse in-product behaviour using tools such as Pendo.
- Roadmap Support: Size opportunities, evaluate experiments, and provide data-driven insights that contribute to product strategy and roadmap prioritisation.
Requirements:
- Experience: 6+ years of experience in product or business analytics, including direct collaboration with product teams.
- Advanced SQL: Strong SQL skills, including multi-CTE queries, window functions, cohort analysis, and querying semi-structured JSON data in Snowflake.
- Data Analysis: Ability to use Python or R when analytical requirements extend beyond what SQL can effectively address.
- Data Warehousing: Production experience with cloud data warehouses, ideally including ClickHouse and Snowflake.
- BI & Reporting: Experience creating datasets and dashboards in Superset, Tableau, or comparable business intelligence platforms, with the ability to audit and understand existing reporting logic.
- Product Analytics: Experience with product analytics platforms such as Pendo, Amplitude, or Mixpanel, including event definition and tracking-plan development.
- Business Reviews: Experience owning recurring metrics packs, business reviews, or analytical reporting relied upon by senior stakeholders.
- Data Quality: Strong attention to data grain, time periods, cohort completeness, and metric definitions, with a disciplined approach to validating figures before they are used for decisions.
- SaaS Metrics: Strong understanding of SaaS metrics including MRR, ARR movements, expansion, contraction, churn, retention, and ARPU.
- Experimentation & Statistics: Sufficient statistical knowledge to design, size, interpret, and communicate experiments accurately, including recognising when results are too uncertain to support a decision.
- Communication: Excellent written English and the ability to communicate complex analysis clearly and concisely to busy stakeholders.
- AI Analytics: Experience analysing AI or LLM products, including token consumption, model costs, or quality evaluation, is a strong advantage.
- Pricing Models: Familiarity with usage-based or hybrid pricing models and their associated analytical and reporting challenges is desirable.
- Data Transformation: Experience with dbt or a comparable data transformation framework is a plus.
- Industry Exposure: Experience with CRM, agency, SMB SaaS, or subscription-based products is beneficial.
- Subscription Analytics: Familiarity with subscription analytics platforms such as ChartMogul is an advantage.
Benefits:
- Competitive compensation within a well-funded and profitable organisation.
- Remote-first working environment.
- Flexible approach designed to support distributed teams.
- Opportunity to work on innovative AI-powered products with global reach.
- High-ownership environment where individuals are encouraged to take initiative.
- Lean, fast-paced product development culture focused on delivering value quickly.
- Opportunities to influence product strategy, roadmap decisions, and business performance.
- Continuous learning and iterative improvement culture.
- Collaborative environment with direct exposure to product leadership.
- Inclusive workplace focused on employee wellbeing and supporting people to do their best work.
- Opportunity to contribute ideas and test new approaches in a culture that values experimentation and innovation.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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