Data Scientist

We are looking for a Data Scientist with strong experience in machine learning engineering and MLOps to manage the end-to-end lifecycle of machine learning models. This role involves everything from experimentation and model development to deployment, monitoring, and optimization within an AWS SageMaker Studio environment. The ideal candidate will collaborate closely with engineering, operations, and product teams to build scalable ML solutions that deliver measurable business value.

Key Responsibilities

• Design, build, and evaluate machine learning models that support business initiatives across operations, marketing, and legal services
• Manage the complete machine learning workflow, including data preparation, feature engineering, model training, validation, deployment, and monitoring
• Develop and maintain MLOps pipelines within AWS SageMaker Studio, including experiment tracking, model versioning, and automated retraining processes
• Monitor model performance in production environments, identify drift or degradation, and implement improvements as needed
• Create clear documentation for methodologies, technical decisions, and model performance metrics to support internal knowledge sharing

Qualifications:

Required

• 3\+ years of experience in data science or a related field with hands-on involvement in ML engineering and MLOps
• Strong Python programming skills for machine learning and data analysis using tools such as pandas, scikit-learn, PyTorch, or TensorFlow
• Practical experience using AWS SageMaker Studio for developing, training, and deploying machine learning models
• Solid understanding of MLOps concepts including pipeline automation, model versioning, monitoring, and drift detection
• Experience writing SQL queries and working with structured data in cloud-based warehouses or relational databases

Preferred

• Industry experience within legal services, collections, or financial services environments
• Familiarity with direct mail marketing, customer segmentation, or targeted campaign modeling
• Experience using AI-assisted development tools such as Claude, GitHub Copilot, Cursor, or similar platforms
• Knowledge of predictive modeling techniques including propensity modeling, uplift modeling, or response prediction
• Exposure to LLM-powered applications or NLP solutions in production environments
• Data engineering experience with platforms and tools such as Dagster, Airbyte, and dbt
• Familiarity with AWS ecosystem services including Glue, Lambda, Redshift, and Step Functions

Benefits

• Medical, Dental, Vision Insurance
• 401(k) with Match
• PTO and Paid Holidays
• Remote Work Environment
• Salary: $110k - $130k

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