Senior Machine Learning Engineer

Job Description: Senior Machine Learning Engineer Role Overview: We are looking for an experienced Senior Machine Learning Engineer with 5+ years of experience in developing, deploying, and optimizing machine learning models for customer analytics and recommendation systems. The candidate will be responsible for building predictive models for customer churn, upsell/cross-sell propensity, customer lifetime value (CLV), revenue contraction, and next-best-action recommendations. The role requires strong expertise in Python, SQL, PySpark, AWS cloud, machine learning, and MLOps frameworks. Key Responsibilities: - Develop, train, validate, and deploy ML models for customer churn, upsell, cross-sell, downsell, and customer lifetime value prediction. - Build next-best-action recommendation and ranking models. - Define model targets, labels, prediction horizons, and customer eligibility criteria in collaboration with business teams. - Perform feature engineering, feature selection, exploratory analysis, and data leakage assessment. - Build scalable feature stores and point-in-time-correct training and scoring datasets. - Develop NLP and sentiment-based features from customer feedback and free-text data. - Implement model validation, probability calibration, and class imbalance handling. - Monitor model performance and stability across customer segments and time periods. - Develop scalable ML pipelines using modern MLOps frameworks. Required Technical Skills: - Programming: Python, SQL, PySpark. - Machine Learning: Machine Learning, Deep Learning, Predictive Modeling, Model Training and Validation. - ML Use Cases: Churn Prediction, Propensity Modeling, Customer Lifetime Value, Recommendation Systems. - NLP: NLP modeling, Sentiment Analysis, Text-based Feature Engineering. - AWS & Data Engineering: AWS S3, Data Lake/Lakehouse Architecture. - Data Technologies: Apache Iceberg, Delta Lake, Dremio. - MLOps: MLflow, Kubeflow, AWS SageMaker, or equivalent frameworks. Experience & Qualifications: - 5+ years of experience in Machine Learning Engineering or Data Science. - Strong hands-on experience in Python, SQL, and PySpark. - Experience developing and deploying end-to-end ML models. - Practical experience in customer analytics and recommendation systems. - Experience with AWS cloud and scalable ML pipelines. - Knowledge of MLOps, model monitoring, and production deployment. - Strong analytical and stakeholder collaboration skills.