Associate Director, Marketing Sciences

Role Summary

Associate Director, Marketing Sciences at Gilead leads the design, development, and scaling of advanced omnichannel data science capabilities to enhance data-informed commercial decision making. The role focuses on modeling HCP engagement patterns, understanding sales and patient-journey dynamics, translating complex commercial questions into rigorous analytical solutions, and delivering measurable impact across brand, sales, and omnichannel initiatives. It also requires scientific and technical leadership to shape omnichannel data sciences strategy and influence senior stakeholders while ensuring production readiness and governance.

Responsibilities

• Lead the design, development, and scaling of omnichannel data sciences capabilities—including sales, content and engagement machine learning models, content-ranking systems, and engagement prediction frameworks—to support data-informed commercial decision making.
• Establish sustainable, reproducible methodologies for building and deploying data science solutions, ensuring long-term maintainability and alignment with enterprise architecture and commercial strategy.
• Partner with Marketing Science measurement teams to incorporate analytical feedback, performance insights, and validation results into model enhancements and roadmap evolution—without owning measurement design itself.
• Conduct functional feasibility assessments for omnichannel solution development and collaborate with IT and Commercial Operations on providing business enhancements for CRM, SFMC and Model Monitoring solutions.
• Serve as a commercial point of contact for enterprise-wide data science policies, model governance, and data science integration workstreams, ensuring that omnichannel data science use cases adhere to regulatory and compliance requirements.
• Collaborate with global affiliates to understand market-specific omnichannel needs and translate them into scalable use cases; act as subject-matter expert for omnichannel and data science driven engagement solutions across regions.
• Partner with Global Commercial teams, Digital, and US Commercial teams to plan and operationalize omnichannel data science enhancement rollouts, ensuring consistency, sustainability, and alignment to global standards.
• Introduce industry best practices in data science, ML engineering, and MLOps—focusing on omnichannel personalization, customer engagement, and commercial analytics.

Qualifications

• Required: Bachelor's degree in Data Science, Computer Science, Statistics, Engineering, Applied Mathematics, or a related quantitative field, and ten (10) years of relevant experience.
• Required: Master’s degree in a relevant quantitative field and eight (8) years of relevant experience.
• Required: PhD in a relevant quantitative field and two (2) years of relevant experience.
• Preferred: Bachelor’s degree with 10+ years, Master’s degree with 8+ years, or PhD with 6+ years of experience in data science, analytics, computer science, or related quantitative fields.
• Preferred: Proven experience developing, deploying, and scaling machine-learning algorithms in commercial or omnichannel environments (e.g., next-best-action, personalization models, engagement prediction).
• Preferred: Demonstrated success operationalizing data science solutions from pilot to production, including data pipeline design, MLOps, model monitoring, and performance optimization.
• Preferred: Experience collaborating with measurement, insights, and commercial analytics teams to integrate validation feedback and refine ML-driven recommendations.
• Preferred: Strong ability to translate complex technical concepts into clear, business-focused narratives tailored for senior commercial stakeholders.
• Preferred: Deep understanding of omnichannel data structures (HCP engagement, CRM, digital behaviors) and familiarity with pharmaceutical commercial processes and compliance considerations.
• Preferred: Ability to influence cross-functional partners, drive alignment, and lead enterprise-scale technical initiatives in a matrixed organization.

Skills

• Data science and ML development, including design and deployment of next-best-action, engagement prediction, and personalization models.
• ML engineering, MLOps, model monitoring, data pipelines; cloud platforms (AWS, Databricks, Azure ML).
• Containerization and orchestration (Docker, Kubernetes).
• Regulatory and compliance considerations in commercial analytics (HIPAA, GDPR, FDA guidance).
• Python, PySpark, MLflow, Airflow, feature store technologies; integration with CRM systems and CDPs.
• Strong communication and storytelling; ability to influence senior stakeholders and work across functions.
• Leadership of enterprise-scale, matrixed programs; ability to translate technical concepts into business outcomes.
• Understanding of HCP engagement data, CRM, digital behaviors; omnichannel data structures.