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

Company H1B Sponsorship