Modeling Expertise:Proven experience in designing and implementing propensity models focused on cross-selling and upselling opportunities.In-depth knowledge of Next Best Action / Next Best Offer systems.Solid understanding of supervised learning methodologies, uplift modeling, and causal inference techniques.Programming & Engineering:Proficient in Python (required); knowledge of R or Scala is advantageous.Experience with distributed computing frameworks such as Spark, Dask, or Ray.Skilled in developing production-level ML pipelines using tools like Airflow, MLflow, or Kubeflow.Strong familiarity with software engineering best practices including version control, CI/CD, and thorough testing.Data Infrastructure:Experience with cloud platforms (AWS/GCP/Azure) and data warehouses (Snowflake, BigQuery, Redshift).Excellent SQL skills; adept at optimizing queries and handling large datasets.MLOps & Deployment:Proven ability to deploy models to production using APIs or streaming technologies (Kafka, Flink).Experienced in model versioning, experiment management, and deployment practices with MLflow, SageMaker, or Vertex AI.Monitoring & Observability:Capable of establishing model monitoring, drift detection, and alerting systems utilizing Prometheus, Grafana, Evidently, or custom dashboards.Familiarity with logging frameworks and performance profiling for ML services.GenAI (Preferred):Experience with Large Language Models, embeddings, prompt engineering, and vector databases (e.g., FAISS, Pinecone).Ability to incorporate GenAI into decision-making systems or customer-facing applications.Leadership & Collaboration:Proven leadership skills to guide senior data scientists while maintaining a hands-on approach.Adept at collaborating with product managers, engineers, and business stakeholders.
Dec 12, 2025