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

Skill:

• Telecom domain must
• Data warehousing SQL Spark framework, pyspark
• Complex
• Big data concept
• Data science concept
• Core DS Fundamentals – ml datascinece
• Advanced Cloud SQL & Tuning
• Business-Centric Evaluation
• Python Ecosystem

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.

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.

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