Product Analyst — PRISM & Ads Platform

About The Role

We’re building a brand-facing platform (PRISM) that helps our partner brands grow on Purplle through operational excellence and data intelligence. Alongside it, we run PAP, Purplle’s in- house ads platform, which turns on-platform inventory into revenue for the business and into growth for brands. You’ll join as a Product Analyst owning the analytics layer across both. You’ll make sure the platform runs without glitches, measure whether brands are adopting it, give brands the data they need to grow, and help build the intelligence layer on top of PRISM. On the ads side, you’ll find ways to grow ad revenue and get the most from every slot of inventory we already have. What you’ll do:

• Keep PRISM glitch-free — define and track the health metrics for every live module (errors, SLAs, data accuracy, report integrity), set up alerts that catch issues before brands or KAMs do, and run root-cause analysis when something breaks. - Automate adoption and efficiency reporting — measure how brands adopt each PRISM module and how much manual effort the platform removes for KAMs and internal teams, and replace recurring manual reports with automated, trusted pipelines and dashboards. - Drive brand growth and experience through data — identify the data and metrics that matter most to brands (sales, funnel, offers, availability, competitiveness) and build reports they can consume directly. - Build the PRISM intelligence layer — define the critical metrics and layer that power recommendations, alerts, and brand health scoring. You own conceptualisation and POCs; the Data Science team supports productionisation and scale. - Own analytics for the Ads platform (PAP) — run performance reporting across ad formats and surfaces, explain what is moving revenue and what is not, and identify avenues for ad revenue growth. - Maximise ad inventory utilisation — track fill rate, sell-through, and yield, and recommend pricing and placement improvements. Work closely with Ad Sales and Ad Ops to surface opportunities and measure how well the strategies they execute perform. - Own outcomes, not just reports — tie every analysis to a decision, a recommendation, and a measurable result, and follow through on whether it worked. - Build with an AI-native mindset — use AI tools to move faster through analysis, documentation, and prototyping, and spot where AI/LLM capabilities can create outsized value inside PRISM and PAP rather than treating AI as an add-on. What we’re looking for:
• 2–4 years of experience in product analytics, business analytics, ad-tech or retail- media analytics, or applied data science, with real ownership of metrics and reporting for a product or business area. - Strong SQL on a cloud data warehouse (BigQuery-style) — complex joins, window functions, and performance-aware queries. - BI and dashboarding fluency — hands-on with tools like Looker, Tableau, Power BI, or Metabase, and the judgement to build dashboards people actually use. - Python and ML fundamentals — pandas and scikit-learn or similar. You can take a use case from framing to a validated POC (forecasting, anomaly detection, scoring, or recommendation models). - Statistical and experimentation rigour — hypothesis testing, A/B test design, and clean pre/post or test/control reads on strategies. - Metric thinking — able to break ambiguous business questions into metric trees, pick the right north-star and guardrail metrics, and define them unambiguously. - Excellent written and verbal communication — you can turn analysis into a clear recommendation for a non-technical audience (brand managers, KAMs, Ad Sales) and write precise metric definitions. - Stakeholder management — able to work with Product, Engineering, Data Science, Ad Sales, Ad Ops, and KAMs, each with different priorities, toward a shared outcome. - Data skepticism — you reconcile and sanity-check numbers, including your own, and flag data issues before they reach a brand. - Bias for action and ownership — you drive things forward independently, ship a useful first version fast, and automate anything you’ve done twice. - AI-native orientation — genuine fluency and enthusiasm for using AI tools in your own workflow. - Prior experience with retail media or ad-tech metrics (fill rate, CTR, ROAS, eCPM, yield management, attribution) is a strong plus. - Exposure to marketplace, B2B SaaS, or vendor-portal analytics (adoption funnels, self-serve metrics, operational SLAs) is a plus. - Familiarity with data pipeline and quality tooling (dbt, Airflow, or similar) is a plus, though not required.

About Company

Founded in 2011, Purplle has emerged as one of India’s premier omnichannel beauty destinations, redefining the way millions shop for beauty. With 1,000+ brands, 60,000+ products, and over 7 million monthly active users, Purplle has built a powerhouse platform that seamlessly blends online and offline experiences.

Expanding its footprint in 2022, Purplle introduced 6,000+ offline touchpoints and launched 100+ stores, strengthening its presence beyond digital. Beyond hosting third-party brands, Purplle has successfully scaled its own D2C powerhouses—FACES CANADA, Good Vibes, Carmesi, Purplle, and NY Bae—offering trend-driven, high-quality beauty essentials.

What sets Purplle apart is its technology driven hyper-personalized shopping experience. By curating detailed user personas, enabling virtual makeup trials, and delivering tailored product recommendations based on personality, search intent, and purchase behavior, Purplle ensures a unique, customer-first approach.

In 2022, Purplle achieved unicorn status, becoming India’s 102nd unicorn, backed by an esteemed group of investors including ADIA, Kedaara, Premji Invest, Sequoia Capital India, JSW Ventures, Goldman Sachs, Verlinvest, Blume Ventures, and Paramark Ventures.

With a 3,000+ strong team and an unstoppable vision, Purplle is set to lead the charge in India’s booming beauty landscape, revolutionizing the way the nation experiences beauty.