Product Analyst — Reactivation and Retention

Core ownership :

You will own the end-to-end reactivation programme at INDmoney — not just the analysis, but the campaign design, execution, and measurement. This means: identifying dormant user segments with the highest reactivation potential, building the behavioural signals that predict reactivation, defining the right message, channel, and timing for each segment, and holding yourself accountable to reactivated ARPU as north star metrics. You will iterate on this programme weekly, not monthly.

Experimentation & measurement

• Design and analyse A/B tests across activation nudges, propensity-triggered campaigns, and reactivation sequences — with correct sample sizing, holdout groups, and guardrail metrics defined before launch
• Measure propensity model lift rigorously — gain charts, KS statistic, AUC — and translate model performance into business terms a PM can act on
• Build a weekly lifecycle health report that is read, not filed — surfaces cohort anomalies, model drift signals, and campaign performance in one place
• Instrument new product surfaces correctly from day one in partnership with data engineering — no retro-fixing of broken event schemas

Core skills

• Statistical fluency — strong working knowledge of logistic regression, decision trees, gradient boosting (XGBoost/LightGBM), model calibration, and evaluation metrics (AUC, KS, gain/lift curves). Knows when a simple model is better than a complex one and can defend that choice
• Propensity modelling ownership — has built propensity or churn models end to end: feature selection and engineering, training and cross-validation, score calibration, deployment to a campaign tool, and measurement of real-world lift. Not just academic exposure — has done this on production data with a business outcome attached
• Cohort design — understands the difference between acquisition cohorts, behavioural cohorts, and propensity-based segments; has built multi-dimensional cohort frameworks that product and marketing teams actually use
• Reactivation campaign ownership — has designed and run dormant user reactivation programmes end to end, including segment definition, channel sequencing, holdout design, and outcome attribution
• SQL fluency — writes complex queries independently: window functions, retention matrices, multi-join event tables, cohort arrays. Feature engineering for models happens in SQL and Python
• LTV and ARPU literacy — comfortable modelling customer lifetime value, understands cohort ARPU vs average ARPU, and can reason about payback periods and unit economics independently

Experience

• 2–5 years in product analytics, growth analytics, or lifecycle marketing analytics at a consumer internet or fintech company
• Has owned a reactivation or retention metric — not just reported on it — and can describe what interventions they ran and what moved the number
• Hands-on experience with a CRM/lifecycle tool (CleverTap, MoEngage, Braze, WebEngage) — has built segments, configured journeys, and read campaign performance reports directly
• Experience in a multi-product platform where cross-sell and product adoption analysis was part of the role
• Has built a churn prediction or reactivation scoring model and translated it into a prioritised outreach list
• Understands fintech-specific dormancy patterns — SIP pause behaviour, portfolio neglect after market drawdowns, seasonal investment spikes
• Personal experience using investment platforms and can articulate what triggers their own return behaviour