InfoVision, Inc.
Lead Data Scientist Propensity & Segmentation (Telecom)
Irving, Texas, USA · Contract
Sponsorship not specifiedDetected 41 days ago
PythonData StructuresSQLBigQueryMachine Learningscikit-learnPandasNumPySparkData ScienceExperimental DesignGIS
About the role
- 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.
- 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.
Responsibilities
- 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.
- 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.
- Deep understanding of traditional ML theory, including class imbalance mitigation, feature selection, probability calibration, and experimental design.
- Write, debug, and optimize complex SQL queries on cloud data warehouses.
- Build, calibrate, and maintain propensity and "take rate" models utilizing gradient boosted trees (e.g., XGBoost, LightGBM) to optimize marketing spend.
- 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.
Requirements
- Ability to evaluate models beyond standard AUC/ROC, focusing on lift charts, precision-recall curves, tier separation, and financial ROI.
- Advanced proficiency in Python, specifically utilizing the traditional data science stack (pandas, NumPy, scikit-learn, XGBoost, LightGBM) within notebook and script-based workflows.
- 3+ years specifically navigating telecom, broadband, wireless, or subscription-based data structures (e.g., understanding ARPU, churn cycles).
- Business-Centric Evaluation: Ability to evaluate models beyond standard AUC/ROC, focusing on lift charts, precision-recall curves, tier separation, and financial ROI.
Company info
- Customer Archetypes: Develop unsupervised clustering and segmentation frameworks to group customers and addresses, enabling hyper-personalized marketing workflows.
Visa & Work Authorization
- If interested, Please share below details with update resume: Full Name: Phone: E-mail: Rate: Location: Visa Status: Availability: SSN (Last 4 digit): Date of Birth: LinkedIn Profile: Availability for the interview: Avai
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