[Remote] Data Scientist – Propensity & Segmentation (Must have Current Telecom) - Remote

Note: The job is a remote job and is open to candidates in USA. Lorven Technologies Inc. is seeking a Lead Data Scientist specializing in Propensity & Segmentation within the Telecom sector. The role involves building and deploying machine learning models while evaluating their effectiveness using business-centric metrics, all within a remote work environment.

Responsibilities

• Bachelor's degree or Masters Degree in Computer science, or a related field, with minimum 10+ Years of relevant experience
• You should have at least 5+ years of professional experience as an applied Data Scientist building and deploying supervised and unsupervised machine learning models
• Must have 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
• Must have 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 Domain 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
• 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)
• Strong analytical and problem-solving skills, with a proven track record of identifying and resolving complex billing issues
• Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences

Skills

• Bachelor's degree or Masters Degree in Computer science, or a related field, with minimum 10+ Years of relevant experience
• At least 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
• Must have 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 Domain 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
• 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)
• Strong analytical and problem-solving skills, with a proven track record of identifying and resolving complex billing issues
• Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences

Company Overview

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