Staff Data Scientist (AI/MLOps)

Location: Remote, United States

Company

hirify.global provides commerce-enablement technology and high-volume fulfillment services, including fulfillment, warehousing, transportation, and software platforms for order management, pre- and post-purchase experiences, and warehouse management.

What you will do

• Own complex modeling problems end to end, from problem framing and exploratory data analysis through production deployment.
• Build predictive models for supply chain, logistics, fulfillment, and consumer applications.
• Define organization-wide data science and MLOps standards, tooling, and infrastructure.
• Embed with engineering teams to integrate, deploy, monitor, and retrain production models.
• Lead technical strategy discussions with engineering and product leadership as a senior individual contributor without direct reports.
• Shape the integration of agentic AI and AI-augmented workflows into data and ML infrastructure.

Requirements

• Expert-level Python and experience writing production code.
• Strong SQL and Spark skills, including Postgres and BigQuery.
• Strong foundations in statistical analysis and machine learning, including causal inference and recommendation systems.
• Experience with production model deployment, monitoring, retraining, model versioning, pipeline orchestration, and drift detection.
• Experience with Google Cloud Platform, including BigQuery, Vertex AI, GKE/Kubernetes, and Cloud Run.
• Git/GitHub experience, collaborative engineering workflows, and the ability to communicate complex concepts clearly to technical and business leadership.

Nice to have

• Logistics, supply chain, or e-commerce experience.
• Recommendation systems or customer profile modeling at scale.
• Real-time model serving and high-availability ML systems experience.
• Elixir, TypeScript, or functional programming experience.
• Kubernetes, CI/CD, and DataOps tooling familiarity.

Culture & Benefits

• Individual-contributor role with no direct reports and influence based on technical depth and cross-team collaboration.
• Opportunity to establish standards and infrastructure used across the organization.
• Work with Google Cloud Platform and Claude as the primary cloud and AI stack.
• Collaborate across supply chain, fulfillment, engineering, product, and consumer-facing technology teams.