Data Scientist

Responsibilities: The role involves owning the full machine learning lifecycle from research and experimentation to deployment and monitoring. You will build MLOps pipelines in AWS SageMaker Studio to deliver models that drive business outcomes across operations and marketing.

Requirements: Requires 3+ years of experience in data science with a strong focus on ML engineering and MLOps. Proficiency in Python, SQL, and AWS SageMaker Studio is essential for managing production-level models.

Key skills: Machine Learning Engineering, MLOps, Python, AWS SageMaker Studio, SQL, Pandas, Scikit-learn, PyTorch, TensorFlow, Model Deployment, Feature Engineering, Production Monitoring, Data Preparation, Experiment Tracking, Model Versioning, Drift Detection

Keywords: Machine Learning, MLOps, AWS SageMaker Studio, Python, SQL, PyTorch, TensorFlow, Scikit-learn, Pandas, Model Registry, Drift Detection, Propensity Modeling, Uplift Modeling, NLP, LLM, Dagster, Airbyte, dbt, AWS Glue, AWS Lambda, AWS Redshift, AWS Step Functions, Customer Segmentation, Direct Mail Modeling, Legal Services, Cloud Data Warehouses, Feature Engineering, Production Monitoring, Agentic Workflows, Claude