Note: The job is a remote job and is open to candidates in USA. Robert Half is 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.
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
Skills
- 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
- 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
Company Overview
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