Senior Machine Learning Engineer

Duration of Contract: 06 months + Possible Extension Key ResponsibilitiesDevelop machine learning models for customer churn, upsell propensity, cross-sell propensity, downsell or right-sizing, customer lifetime value, and next-best-action recommendations.Work with business analysts, product owners, and Data Engineers to convert business definitions into model targets, eligibility populations, prediction horizons, and measurable customer outcomes.Define target and label logic for customer churn, product churn, revenue contraction, product acquisition, product upgrade, renewal, and customer-value use cases.Work with Data Engineers to build point-in-time-correct training and batch-scoring datasets from governed feature-store definitions.Perform exploratory analysis, feature selection, leakage assessment, class-imbalance handling, model development, probability calibration, and time-based model validation.Develop and validate sentiment-based features from eligible customer free text and assess their contribution to churn and propensity models.Develop next-best-action candidate generation and recommendation-ranking logic using model scores, customer value, expected margin, product eligibility, customer benefit, and business priorities.Validate model performance and stability across industry, company size, customer-value bands, product portfolios, and time periods. Required / Preferred SkillsPythonPySparkSQLMachine learning model development, training and validationFeature engineering and target or label creationAWS S3 and data-lake or lakehouse architectureApache Iceberg/Delta table and Dremio experience will be an added advantageKubeflow, MLflow, SageMaker, or equivalent MLOps experience and NLP modeling will be an added advantage