Data Scientist

The client is seeking a Data Scientist with deep expertise in Generative AI, agentic architectures, and MLOps to design, build, and scale end to end AI solutions while embedding Responsible AI practices across the full development lifecycle. This role requires hands on MLOps maturity, not just model building, the candidate will own how models move from experimentation into production and stay reliable once they get there., * Design and deploy end to end RAG solutions and autonomous AI agents in cloud and enterprise environments

• Build and scale machine learning and AI models on cloud platforms, primarily AWS or Azure
• Develop and maintain MLOps pipelines to support model deployment, monitoring, versioning, and governance
• Own CI/CD for ML workflows, including automated retraining, model registry management, and rollback procedures
• Implement model monitoring for drift, performance degradation, and data quality issues in production
• Apply statistical modeling techniques to solve complex business problems
• Collaborate with stakeholders across the organization to translate requirements into scalable AI solutions
• Embed Responsible AI practices across model development, deployment, and governance workflows
• Contribute across the full development lifecycle, from experimentation through production release

Must Haves:

• Location: candidate must be based in Pacific, Mountain, or Central time zone. This is a hard requirement, not a preference.
• Minimum 4 years of experience working specifically as a Data Scientist (title and scope must match, not adjacent titles like Data Analyst or ML Engineer alone)
• Must currently or most recently hold a Data Scientist title (Data Scientist, Senior Data Scientist, Staff Data Scientist, Principal Data Scientist, etc.). Candidates whose current or most recent role carries a different title (Data Analyst, ML Engineer, Analytics Engineer, etc.)
• Minimum 3 years of hands on MLOps experience, specifically model deployment, monitoring, and lifecycle management in production environments (not just model development or notebooks)
• Direct experience with at least one MLOps tooling stack such as MLflow, Kubeflow, SageMaker Pipelines, or Azure ML Pipelines
• Master’s degree in a STEM field
• 4 years of proficiency in SQL
• 4 years of proficiency in Python
• Hands on experience with AWS or Azure cloud platforms
• Proficiency with Git for version control
• Strong communication skills with demonstrated ability to work cross functionally with stakeholders

Nice to Haves:

• Experience with Snowflake for data warehousing and analytics
• Hands on experience with AWS specifically, in addition to general cloud proficiency
• Startup or fast paced environment mindset with comfort navigating ambiguity
• Active personal use of AI tools and familiarity with the evolving AI landscape