Data AnalystExp : 6yrs to 15yrs
Work Location : Hyderabad/Noida/Chennai/Bangalore
Shift & Mode : 2pm to 11pm -(3 Days WFO)
Specialization: Must have: SQL
Good to have: Data Analysis
Key Responsibilities: Must-HaveAdvanced SQL: build member-level feature tables from account/product/transaction data (joins, window functions, performance tuning).
Python analytics + ML: pandas/numpy + scikit-learn (or equivalent) for modeling and reproducible analysis.
Transaction feature engineering: create behavioral features/flags from raw transactions (RFM, rolling windows, inflow/outflow mix, category mix, volatility).
Data prep robustness: missing-data handling + outlier treatment (imputation/missing indicators, winsorization/capping, robust scaling/log transforms).
Segmentation modeling: clustering + (optional) hybrid rule+cluster segmentation; ability to select/validate segments (K selection + quality metrics) and produce stable segments.
Segment profiling: translate clusters into interpretable segment definitions (drivers, behaviors, sizing, KPIsPropensity/classification modeling: logistic regression + tree-based methods; evaluation (AUC/KS/lift, calibration, temporal validation).
Time-series fundamentals: trend/seasonality/cycle detection to support cash-flow insights.
Nice-to-HaveAdvanced clustering: GMM/mixture, hierarchical, DBSCAN/HDBSCAN; mixed-type distance strategies.Operationalization: repeatable pipeline (feature refresh + scoring + versioning) + monitoring (segment drift/KPI drift) and calibration approachCloud/big-data tooling: Snowflake

Keyskills: Banking Analytics Data Analytics Python SQL Segmentation Data Analysis