Position: Lead Data Scientist – Propensity & Segmentation (Telecom)
Location: Irving, TX (3 days hybrid 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.
WHAT YOU WILL DO:
Hands-on Feature Engineering: Write, debug, and optimize complex SQL queries on cloud data warehouses. You will build clean feature sets from raw, massive source tables spanning customer billing, network performance, competitive footprint, and geographic data.
Predictive & Behavioral Modeling: Build, calibrate, and maintain propensity and "take rate" models utilizing gradient boosted trees (e.g., XGBoost, LightGBM) to optimize marketing spend.
Customer Archetypes: Develop unsupervised clustering and segmentation frameworks to group customers and addresses, enabling hyper-personalized marketing workflows.
Enforce Core DS Rigor: Engineer features utilizing strict time-series windows to rigorously protect against data leakage, lookahead bias, and overfitting.
Model Explainability & Performance: Evaluate and explain model mechanics using SHAP and feature importance. Monitor models in production to detect and remediate data and concept drift.
Experimental Design: Collaborate with marketing teams to design A/B tests and randomized control trials (RCTs) to measure true incremental lift and isolate campaign performance from organic consumer behavior.
Deliver Actionable Outcomes: Cleanly package outputs into business-ready deliverables, including feature dictionaries, performance tier charts, and scored target lists.
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.
REQUIRED SQL & BIG DATA SKILLS:
Advanced Cloud SQL & Tuning: Expert-level SQL proficiency on cloud data warehouses (BigQuery, Snowflake, or Redshift). You must know how to diagnose and fix poorly performing queries, optimize complex window functions, and handle heavy aggregations on tens of millions of rows efficiently.
Memory Optimization: Practical experience handling datasets that exceed local memory constraints using batching, sampling, or large-scale data frameworks (e.g., PySpark, Dask, or warehouse-native tools like BigQuery ML/Snowpark).
REQUIRED MACHINE LEARNING & EXPERIENCE:
Experience: 5+ 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.
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