product analyst

measure feature and product impact across L0 (business outcomes), L1 (product flow), and L2 (feature-level micro-events) metrics post-launch, and feed structured inputs to the BI team for reporting investigate questions about current users by slicing cohorts and user segments to answer "who is this user, what are they doing, and why" with data track and analyse A/B experiments executed via ProductOps, report variant performance, and give PMs a clear, data-backed recommendation on whether to kill or scale identify data gaps and outside-in signals, including share-of-wallet benchmarks from the analytics team, to surface areas of underperformance and opportunity for the PM to act on ensure data is tagged and instrumented correctly ahead of every feature launch, work with engineering and ProductOps to confirm data readiness before go-live own the measurement of conversion, TAT, and drop-off metrics across user journeys, and proactively flag anomalies before anyone asks communicate findings clearly to PMs and cross-functional stakeholders, not just tables and charts, but a point of view on what the data means and what should happen next use AI tools (Claude, ChatGPT, Copilot) to accelerate your own analysis, summarising outputs, generating commentary on metric movements, classifying data at scale. spot opportunities where the AI or BI team could build something more permanent, and bring those requests forward are proficient in SQL you can write queries independently to extract, filter, aggregate, and join data without hand-holding are comfortable in Python (pandas, numpy) for data cleaning, cohort analysis, and automating repetitive tasks in your own workflow have strong excel skills pivot tables, vlookup/ index-match, and basic modelling are part of your daily workflow can translate data into a clear point of view, you don’t just present numbers, you frame what they mean and what should happen next communicate findings clearly to non-technical stakeholders you can walk a PM or business lead through an analysis without losing them in methodology are comfortable with ambiguity: you can define the right question from a vague brief and structure your own analysis approach hold a bachelor’s or master’s degree in engineering, mathematics, statistics, economics, or a related quantitative field have a basic understanding of A/B testing principles, what makes an experiment valid, what statistical significance means, and how to read results without over-claiming are curious about using AI tools to work faster, you don’t need prior experience, but you should be someone who naturally asks “can I automate this?” have exposure to fintech, lending, or BFSI products — familiarity with bureau flows, acquisition funnels, or collections will reduce your onboarding time significantly understand how product events are instrumented, what data tagging means and what data readiness before a launch actually requires have worked with competitive benchmarking or market-sizing exercises, share of wallet, category demand analysis, or similar outside-in research