Measure feature and product impact across L0-L2 metrics post-launch; feed inputs to BI.
Investigate user questions by slicing cohorts and segments to answer who/what/why.
Track A/B experiments via ProductOps; report results and provide data-backed PM recommendations.
Ensure data tagging and instrumentation before launches; coordinate with Eng/ProductOps.
Own conversion, TAT, and drop-off metrics; flag anomalies proactively.
Communicate findings to PMs and stakeholders with actionable insights.

🎯 Requirements

Proficient in SQL; write queries to extract, filter, aggregate, join data.
Comfortable in Python (pandas, numpy) for cleaning, cohorts, automation.
Strong Excel skills (pivot, vlookup/index-match) and basic modelling.
Translate data into a clear POV; frame meaning and next steps.
Communicate findings to non-technical stakeholders; guide PMs through analyses.
Degree in engineering/math/stats/economics; basic A/B testing knowledge; AI-curious.

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