Role Overview We're seeking a Data Scientist with hands-on experience in machine learning engineering and MLOps. In this role, you'll own the full model lifecycle - from research and experimentation through deployment, monitoring, and iteration. You'll work within our AWS SageMaker Studio environment and collaborate closely with engineering, operations, and product teams to deliver models that drive measurable business outcomes.
Key Responsibilities - 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
Requirements Required - 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
Nice to Have - Experience in the legal, collections, or financial services industry
- Background in targeted mail marketing, direct mail modeling, or customer segmentation
- Familiarity with AI coding agents and agentic development workflows (e.g., Claude, Copilot, Cursor, or similar tools)
- Experience with propensity modeling, uplift modeling, or response prediction
- Exposure to LLM-based workflows or applied NLP in a production setting
- Data engineering experience with modern tooling such as Dagster, Airbyte, and dbt
- Familiarity with AWS data services including Glue, Lambda, Redshift, and Step Functions
Compensation & Benefits - 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
Salary Range: $110,000-$130,000 depending on experience
