Key highlights of the role are listed below (purely indicative and not limiting):
Own, develop, and maintain advanced propensity models (XGBoost, LightGBM, Random Forest) focused on Next Best Action (NBA), Next Best Offer (NBO), and cross-sell optimization.
Build predictive analytics frameworks to trace digital adoption, app behavior, and user journey micro-segments.
Conduct deep-dive behavioral clustering and sequence analysis using Unsupervised Learning (K-Means, Agglomerative Clustering) to trace engagement trends.
Advanced SQL & Data Engineering
Write highly optimized, complex SQL queries on distributed transactional and clickstream data frameworks.
Maintain absolute Data Reconciliation; ensure digital log data matches and reconciles perfectly against core backend transaction databases before presentation.
Automate custom feature-engineering pipelines using Python and cloud-based automation environments.
Strategic Insights & Visualization
Translate complex model scores and micro-segmentation insights into executive-ready presentations and automated dashboards (Qlik Sense or Tableau).
Author accurate technical Business Requirement Documents (BRDs) to bridge the gap between analytics models and downstream real-time CRM orchestration tools.
Gen AI & NLP Integration
Explore the integration of LLMs and NLP applications to scan unstructured interaction logs, customer memo fields, or chat logs to catch latent behavior signals and offer preferences.
Applicants should possess the following attributes:
Personalization Specialist: Proven expertise in implementing actionable propensity models into live business/campaign loops.
Agile Sector Experience: Open to candidates from high-velocity data backgrounds (E-commerce, FinTech, SaaS, Gaming, or Consumer Tech Startups) who excel in rapid testing environments.
Investigative & Behavioral Mindset: Possesses a curious perspective that reads past straightforward status codes. Treats data events (like "Dormant Apps" or specific navigation drops) as complex behavioral puzzles to resolve rather than plain flags.
Expert in Python: (Pandas, NumPy, Scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow).
Expert in SQL: Deep understanding of structured/unstructured query structures and script performance tuning.
Knowledge of Visualization: Tools like Tableau or Qlik Sense.
Cloud Infrastructure: Robust hands-on exposure to cloud data architectures (GCP/BigQuery, AWS/Redshift, or Snowflake) is a big plus.