Lead Data Scientist – Propensity & Segmentation (Telecom)

Hi, Please review the below job requirement and let me know if you are good to submit with the below details filled and your latest resume ASAP. Job Title: Lead Data Scientist – Propensity & Segmentation (Telecom) Location: Irving TX-Onsite ROLE SUMMARY • We build the propensity models and customer segmentation frameworks that drive how we target, acquire, and retain millions of households. This is a 100% hands-on role for a seasoned Data Scientist who loves digging into data and owning execution from end to end. We are looking for someone who can write highly optimized, large-scale SQL feature queries, apply rigorous traditional machine learning methods (avoiding rookie pitfalls like data leakage or uncalibrated models), and turn raw data into high-value targeting lists for marketing. • If you are a practitioner who thrives on optimizing data pipelines, mastering telecom data structures, and applying core data science principles to large-scale datasets, this role is for you. REQUIRED MACHINE LEARNING & EXPERIENCE • Experience: 15+ years of professional experience as an applied Data Scientist building and deploying supervised and unsupervised machine learning models. • Core DS Fundamentals: Deep understanding of traditional ML theory, including class imbalance mitigation, feature selection, probability calibration, and experimental design. • Business-Centric Evaluation: Ability to evaluate models beyond standard AUC/ROC, focusing on lift charts, precision-recall curves, tier separation, and financial ROI. • Python Ecosystem: Advanced proficiency in Python, specifically utilizing the traditional data science stack (pandas, NumPy, scikit-learn, XGBoost, LightGBM) within notebook and script-based workflows. TELECOM & GEOSPATIAL REQUIREMENTS (MUST HAVE) • Telecom Domain Expertise: 3+ years specifically navigating telecom, broadband, wireless, or subscription-based data structures (e.g., understanding ARPU, churn cycles). • Geospatial Literacy: Practical experience using spatial SQL functions (e.g., BigQuery GIS, PostGIS, H3/S2 spatial indexing) to join and analyze location-based data like lat/long coordinates, wire centers, or census tracts.