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Job Summary:
Gilead Sciences is dedicated to creating a healthier world through innovative therapies for significant health challenges. The AI & ML Ops Lead will focus on designing, developing, and deploying data science solutions that enhance commercial efficiency in Kite’s Commercial line of business, collaborating with various teams to ensure effective ML operations and governance.
Responsibilities:
• Model Lifecycle Management: Develop and maintain pipelines to transition models from experimentation to production, including packaging, CI/CD, automated testing, and deployment. Support model serving for patient identification, alignment prediction, next-best-action engines, and competitive intelligence models.
• Data Pipeline Development: Design robust batch and streaming data workflows; integrate, define, and manage feature sets, lineage, and reuse to support AI/ML initiatives.
• Production Operations & Monitoring: Ensure reliability and scalability of ML systems; implement effective logging, tracing, and alerting. Establish monitoring for model performance, data drift, bias, and service health. Monitor data quality across rare disease data feeds, where small population sizes amplify the impact of anomalies.
• Agent Workflow Development: Collaborate with data scientists and commercial stakeholders to decompose complex business workflows into agent-executable workstreams. Define boundaries between agent execution and human data science judgment.
• Instruction Architecture & Prompt Engineering: Design and maintain prompt architectures, agent skills, memories, and context injection patterns. Author structured coding instructions that translate commercial analytics requirements into precise agent directives with clear acceptance criteria.
• Build agentic AI systems that autonomously detect anomalies in commercial data, such as competitive switching, patient discontinuation signals, and payer access changes. These systems generate hypotheses and push recommended actions to stakeholders and CRM systems.
• Token Economics & Cost Optimization: Optimize agent execution for cost efficiency—manage context window utilization, minimize token consumption, and design instruction patterns that reduce iteration cycles. Monitor token economics per workstream to balance capability with budget.
• Model, Agent & Data Governance: Implement version control, approvals, documentation, and audit trails for datasets, code, models, and agent instructions. Ensure all AI/ML outputs are explainable, auditable, and compliant with HIPAA/PHI, GDPR, FDA promotional regulations, and REMS requirements. Enforce secrets management, role-based access control, network policies, and data protection for agents operating on sensitive healthcare and commercial data within the enterprise perimeter.
• Cross-functional Partnership: Work closely with data scientists, commercial analysts, and stakeholders across Brand, Market Access, Patient Services, and Field teams. Provide frameworks, templates, and guardrails that accelerate analytics delivery.
• Testing & Validation: Demonstrate a strong focus on testing by setting up frameworks for both traditional ML models and agent-generated code. Design validation pipelines with automated quality gates, including type checking, linting, integration tests, and contract tests.
• Documentation & Release Management: Develop clear, detailed guides, operational playbooks, and user instructions. Coordinate releases with commercial operations and IT; maintain runbooks, rollback strategies, and change tickets.
Qualifications:
Required:
• Bachelor's Degree and Ten Years’ Experience
• Masters' Degree And Eight Years’ Experience
• PhD And Two Years’ Experience
• Model Lifecycle Management: Develop and maintain pipelines to transition models from experimentation to production, including packaging, CI/CD, automated testing, and deployment. Support model serving for patient identification, alignment prediction, next-best-action engines, and competitive intelligence models.
• Data Pipeline Development: Design robust batch and streaming data workflows; integrate, define, and manage feature sets, lineage, and reuse to support AI/ML initiatives.
• Production Operations & Monitoring: Ensure reliability and scalability of ML systems; implement effective logging, tracing, and alerting. Establish monitoring for model performance, data drift, bias, and service health. Monitor data quality across rare disease data feeds, where small population sizes amplify the impact of anomalies.
• Agent Workflow Development: Collaborate with data scientists and commercial stakeholders to decompose complex business workflows into agent-executable workstreams. Define boundaries between agent execution and human data science judgment.
• Instruction Architecture & Prompt Engineering: Design and maintain prompt architectures, agent skills, memories, and context injection patterns. Author structured coding instructions that translate commercial analytics requirements into precise agent directives with clear acceptance criteria.
• Build agentic AI systems that autonomously detect anomalies in commercial data, such as competitive switching, patient discontinuation signals, and payer access changes. These systems generate hypotheses and push recommended actions to stakeholders and CRM systems.
• Token Economics & Cost Optimization: Optimize agent execution for cost efficiency—manage context window utilization, minimize token consumption, and design instruction patterns that reduce iteration cycles. Monitor token economics per workstream to balance capability with budget.
• Model, Agent & Data Governance: Implement version control, approvals, documentation, and audit trails for datasets, code, models, and agent instructions. Ensure all AI/ML outputs are explainable, auditable, and compliant with HIPAA/PHI, GDPR, FDA promotional regulations, and REMS requirements. Enforce secrets management, role-based access control, network policies, and data protection for agents operating on sensitive healthcare and commercial data within the enterprise perimeter.
• Cross-functional Partnership: Work closely with data scientists, commercial analysts, and stakeholders across Brand, Market Access, Patient Services, and Field teams. Provide frameworks, templates, and guardrails that accelerate analytics delivery.
• Testing & Validation: Demonstrate a strong focus on testing by setting up frameworks for both traditional ML models and agent-generated code. Design validation pipelines with automated quality gates, including type checking, linting, integration tests, and contract tests.
• Documentation & Release Management: Develop clear, detailed guides, operational playbooks, and user instructions. Coordinate releases with commercial operations and IT; maintain runbooks, rollback strategies, and change tickets.
Preferred:
• Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field, or equivalent experience.
• Experience: 3–6+ years in MLOps, Data Engineering, or ML platform roles, with a proven track record of deploying ML solutions at scale. At least 2+ years building complex data science or large-scale analytics solutions.
• Programming: Proficiency in Python and SQL; familiarity with TypeScript/JavaScript or a systems language (Go, Rust). Experience with TDD, CI/CD pipelines, and code quality standards.
• CI/CD & Infrastructure: Experience with CI/CD tools (e.g., GitHub Actions), containerization (Docker), and cloud infrastructure concepts.
• ML Tools: Hands-on experience with model packaging and serving frameworks (e.g., SageMaker, Databricks MLflow), experiment tracking, and model registry tools.
• Data Technologies: Proficiency with Databricks distributed processing (Spark), data orchestration (Airflow), MLflow, etc.
• AI/Agent Tools: Hands-on experience with AI coding tools (Claude Code, GitHub Copilot, Cursor, or equivalent) and Cortex AI or comparable LLM serving platforms. Working understanding of how LLMs reason about code and familiarity with prompt engineering as an engineering discipline.
• Security & Compliance: Understanding of data privacy and security in healthcare; experience with secrets management, audit controls, and compliance frameworks (HIPAA, SOC 2, 21 CFR Part 11).
• Systems Thinking: Ability to design systems that scale across both
• Domain Experience: Knowledge of pharmaceutical commercial analytics in CGT or specialty pharma—HCP/HCO profiling and targeting, patient identification, call planning, demand forecasting, specialty pharmacy data, and omnichannel measurement.
• CGT Data Expertise: Experience with IQVIA (LAAD, Symphony, NPA), Veeva CRM, MMIT, Model N, specialty pharmacy dispense data, claims/RWD, and high-value-per-patient environments.
• Agent System Design: Experience designing multi-agent workflows, orchestration patterns, and autonomous systems for enterprise applications. Familiarity with MCP (Model Context Protocol) and agent interoperability frameworks.
• Performance & Scalability: Experience with high-throughput inference, batch scoring at scale, low-latency APIs, and horizontally scalable agent workloads.
• Enterprise Integration: Experience integrating with Veeva, Salesforce, Microsoft 365, and ServiceNow APIs for end-to-end automation.
• Communication & Collaboration: Excellent verbal and written communication skills; ability to present complex findings to both technical and non-technical audiences, with a strong orientation toward teamwork in a fast-paced, regulated environment.
Company:
Gilead Sciences develops and markets biopharmaceutical therapies, focusing on antiviral, oncology, and inflammatory diseases. Founded in 1987, the company is headquartered in Foster City, USA, with a team of 10001+ employees. The company is currently Late Stage.