Key Responsibilities

Product Strategy & Innovation

• Shape and own the product roadmap for AI solutions that power next-gen commercial capabilities — including intelligent targeting, omnichannel orchestration, and HCP engagement optimization.
• Drive innovation by scouting new AI technologies, validating ideas through MVPs and pilots, and scaling high-value solutions.
• Translate commercial objectives into actionable technical requirements and solution architectures.

Technical Delivery & Solution Ownership

• Lead cross-functional teams (data science, data engineering, MLOps, digital product) to build, deploy, and operate production-grade AI solutions.
• Oversee technical delivery from data ingestion and model development to deployment, monitoring, and retraining.
• Ensure models are performant, scalable, secure, and seamlessly integrated into platforms such as CRM and content engines.

Stakeholder Management & Business Alignment

• Partner with commercial stakeholders (field teams, marketing, analytics) to define needs, align priorities, and drive adoption.
• Act as a translator between business and technical teams — clearly communicating trade-offs, timelines, and value.
• Provide thought leadership and influence stakeholders at multiple levels, from working teams to senior leadership.

Compliance & Responsible AI

• Ensure solutions align with Novartis standards and external regulations
• Embed ethical AI principles into product design — including fairness, transparency, and accountability.

Core Technical Focus Areas

• AI/ML Development: Supervised learning, NLP, recommender systems, experimentation frameworks.
• Data Infrastructure: Scalable pipelines, real-time data ingestion, structured/unstructured healthcare data.
• MLOps: Model deployment, versioning, monitoring, and retraining workflows (MLflow, Airflow, SageMaker, etc.).
• Integration: REST/gRPC APIs, integration with CRM (e.g., Veeva), content platforms, and campaign engines.
• Cloud Platforms: AWS, Azure, or GCP environments for AI/ML workloads and data operations.

What You’ll Bring

• 5+ years of product management experience, with 2+ years in AI/ML or data-centric product delivery.
• Strong technical foundation and experience working closely with engineering and data science teams.
• Demonstrated ability to deliver AI products from concept to production, including service ownership.
• Strong stakeholder management skills, with the ability to influence across technical and business domains.
• Experience in healthcare, life sciences, or other regulated environments is a plus.