EXL - Data Scientist - Marketing Analytics

About the job : Position Overview : We are seeking a skilled Modeler to contribute to the development and optimization of marketing analytics models.You will work within a cross-functional team to build propensity models, next-best-action (NBA) engines, customer segmentation frameworks, and campaign response models that power data-driven marketing strategies.You will apply best practices in model explainability, fairness testing, and lifecycle governance to ensure high-quality, compliant model outputs.Key Responsibilities : - Develop and optimize propensity models, segmentation frameworks, and campaign response models using supervised and unsupervised machine learning techniques.- Conduct bias testing and implement model explainability methods (e.g., SHAP, LIME) to support model approval and governance workflows.- Perform exploratory data analysis and feature engineering to identify meaningful predictors of customer behavior.- Support the full model lifecycle including development, documentation, validation support, performance monitoring, and periodic re-validation.- Collaborate with marketing and data teams to understand business objectives and translate them into modeling requirements.- Prepare clear, concise model documentation including methodology overviews, performance summaries, and limitation disclosures.- Monitor deployed models for data drift, performance degradation, and champion-challenger evaluation.- Contribute to A/B test design and campaign measurement analyses to evaluate marketing effectiveness.- Stay current on advances in marketing data science, uplift modeling, and customer analytics.Qualifications & Experience : - 3 - 6 years of experience in data science, analytics, or quantitative modeling with a focus on customer or marketing analytics.- Hands-on experience building and evaluating classification and regression models for propensity scoring or segmentation.- Proficiency in Python with working knowledge of libraries such as scikit-learn, XGBoost, LightGBM, pandas, and numpy.- Familiarity with model explainability tools (SHAP, LIME) and basic concepts of model fairness and bias testing.- Experience with SQL and large-scale data platforms for data extraction and feature preparation.- Understanding of model validation principles and documentation standards.- Strong analytical and problem-solving skills with attention to detail.- Effective written and verbal communication skills for presenting model results to both technical and business stakeholders.- Bachelor's degree (Master's preferred) in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.Model Lifecycle & Governance : This role supports end-to-end model lifecycle management.Responsibilities encompass model development, independent validation and assessment, performance optimization, monitoring, documentation, and governance - ensuring all models adhere to applicable standards and remain fit-for-purpose throughout their operational life.