Develop, train, and evaluate machine learning models to solve business problems across operations, legal services, and marketing
Own the full ML lifecycle: data preparation, feature engineering, model training, validation, deployment, and monitoring
Build and maintain MLOps pipelines using AWS SageMaker Studio, including experiment tracking, model registry, and automated retraining workflows
Partner with product and operations teams to translate business requirements into data science solutions
Monitor deployed models in production, identify performance degradation, and drive continuous improvement
Document methodologies, model performance benchmarks, and technical decisions for internal knowledge sharing
Stay current with advances in ML and data science tooling, and advocate for best practices across the team

3+ years of experience in data science or a closely related role, with demonstrated ML engineering and MLOps responsibilities
Strong proficiency in Python for data science and ML development (pandas, scikit-learn, PyTorch or TensorFlow)
Hands-on experience with AWS SageMaker Studio for model development, training, and deployment
Solid understanding of MLOps principles: model versioning, pipeline automation, drift detection, and production monitoring
Experience with SQL and working with structured data in cloud data warehouses or relational databases
Proven ability to translate complex data science findings into clear, actionable insights for non-technical stakeholders
Strong self-direction and communication skills suited for a remote work environment

Fully remote position with flexible working hours
Comprehensive health, dental, and vision insurance
401(k) with company match
Paid time off and company holidays
Opportunity to shape data science strategy at a growing, industry-leading company

Data Scientist

Key skills