Senior Machine Learning Engineer – Customer Analytics & Recommendation Systems

ROLE OVERVIEW We are seeking an experienced ML Engineerin Helsinki. This role focuses on developing, training, validating, and implementing machine learning models for customer churn, propensity, lifetime value, and recommendation engines. The ideal candidate will work closely with data engineering and business teams to construct scalable feature stores, build predictive pipelines, and leverage modern MLOps frameworks. 2. TECHNICAL COMPETENCIES NEEDED SQL, Pyspark, ML-DL-NLP, AWS S3, Python, PySpark, Machine learning model development, training and validation, Feature engineering and target or label creation, AWS S3 and data-lake or lakehouse architecture, Apache Iceberg-Delta table and Dremio experience, Kubeflow, MLflow, SageMaker, or equivalent MLOps experience, NLP modeling 3. KEY RESPONSIBILITIES Develop 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.