Senior Churn Propensity Modeling Specialist Job Abu Dhabi UAE/Dubai,IT/Tech

Job Title:

Senior Churn Propensity Modeling Specialist

Location: United Arab Emirates (Remote)

Employment Type: Full-Time

Work Arrangement: 100% Remote — Work From Home

Level: Senior

About the Role

We are seeking an experienced Senior Churn Propensity Modeling Specialist to develop, validate, and optimize predictive models that identify customers at risk of leaving a business. The successful candidate will use statistical analysis, machine learning, and customer behavioral data to uncover churn patterns, estimate retention risk, and support targeted customer engagement strategies. This fully remote role requires strong technical expertise and the ability to translate complex model outputs into actionable business recommendations.

Key Responsibilities

Develop and maintain predictive models to estimate customer churn probability across relevant customer segments.
Analyze customer behavior, transaction history, engagement patterns, service usage, and account activity to identify churn indicators.
Engineer relevant features from structured and unstructured customer datasets.
Apply statistical techniques and machine learning algorithms to improve churn prediction accuracy.
Establish model development standards, baseline benchmarks, and performance evaluation frameworks.
Validate model performance using appropriate metrics, including precision, recall, F1-score, ROC-AUC, PR-AUC, and lift.
Assess model calibration, stability, generalizability, and predictive reliability across customer segments.
Conduct feature importance analysis and explain model outputs to business stakeholders.
Identify data-quality issues, leakage risks, sampling bias, and changes in customer behavior that may affect predictions.
Perform model monitoring and investigate performance degradation or changes in churn patterns.
Collaborate with customer relationship management, marketing, product, data engineering, and retention teams.
Translate model predictions into customer risk segments and prioritized retention opportunities.
Support the design and evaluation of targeted retention campaigns and customer intervention strategies.
Conduct controlled experiments to assess the effectiveness of model-driven retention initiatives.
Maintain technical documentation covering datasets, assumptions, model versions, validation results, and limitations.
Recommend improvements to data pipelines, feature stores, and automated model deployment processes.

Qualifications and Experience

Bachelor's or master's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or a related quantitative discipline.
Significant professional experience in predictive modeling, customer analytics, machine learning, or churn analysis.
Strong understanding of classification algorithms, statistical modeling, and predictive analytics.
Practical experience developing customer churn or propensity models.
Proficiency in Python or R and relevant machine learning libraries.
Strong SQL skills and experience working with large customer datasets.
Knowledge of feature engineering, data preprocessing, model selection, and hyperparameter optimization.
Experience with model evaluation, calibration, validation, and performance monitoring.
Understanding of customer lifecycle analytics, segmentation, retention metrics, and behavioral patterns.
Familiarity with cloud-based data platforms, machine learning pipelines, and model deployment practices.
Ability to communicate technical findings and model limitations to non-technical stakeholders.
Strong analytical thinking, problem-solving, and attention to detail.
Ability to manage projects independently and collaborate effectively in a remote working environment.

What We Offer

100% remote working arrangement within the stated employment framework.
Competitive compensation based on qualifications and professional experience.
Opportunities to work on advanced predictive analytics and machine learning initiatives.
Professional development and continuous learning opportunities.
Collaborative environment with cross-functional and international teams.