Lead, Decision Science and AI Enablement COE

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Job Description

OBJECTIVES / PURPOSE

We are seeking an innovative and experienced AI/ML leader to join Takeda's newly established GCC Commercial Analytics & Insights (CA&I) organization in India as the Decision Science and AI Enablement COE Lead. This Center of Excellence (COE) role is foundational in building and scaling Takeda's centralized decision science and AI capabilities to drive data-driven commercial outcomes across all Business Units.

• Lead the Decision Science & AI Enablement Center of Excellence within Takeda’s GCC Commercial Insights & Analytics organization in India, providing centralized AI/ML capabilities for US and global commercial teams.
• Partner with US and International I&A, brand, omnichannel, operations, and access teams to identify high‑value use cases and embed model‑driven decisioning into business workflows (e.g., patient identification, HCP targeting, next‑best‑action).
• Set and execute the AI/ML strategy and roadmap for commercial analytics, ensuring solutions are robust, explainable, compliant, and aligned with priority brands and portfolios.
• Build and develop India‑based pods of data scientists and ML engineers that operate as an embedded extension of US/Global I&A, fostering a culture of scientific rigor, innovation, and continuous learning.

ACCOUNTABILITIES

AI/ML strategy and delivery

• Define and maintain the commercial AI/ML roadmap in partnership with US, global I&A and business stakeholders, spanning ideation, prioritization, and delivery of high‑impact use cases.
• Lead development, validation, and deployment of models (e.g., classification, regression, NLP, recommendation, deep learning) using commercial datasets such as claims, EMR, specialty pharmacy, CRM, and digital engagement.
• Ensure models are documented, monitored, and governed appropriately, with clear performance metrics, explainability, and lifecycle management.

Personalization and decision frameworks

• Design and evolve decisioning frameworks (e.g., next‑best‑action / next‑best‑channel, targeting and segmentation engines) that can be re‑used across brands and markets.
• Partner with DD&T, omnichannel, field, and marketing operations to integrate models into production systems and campaign workflows, with appropriate testing and control groups.
• Define and track KPIs to measure the business impact of AI‑enabled decisions, using experimentation and A/B testing where appropriate.

Innovation and GenAI

• Lead exploration and adoption of Generative AI and LLM‑based solutions for commercial use cases such as content generation, literature and insight synthesis, and intelligent assistants for analytics teams.
• Ensure that the team adheres to the standards and guardrails for responsible AI use, including risk assessment, compliance, and privacy considerations
• Maintain external relationships (vendors, platforms, academic partners) to keep the COE at the forefront of AI innovation.

Talent development and COE excellence

• Build, lead, and develop India‑based pods of data scientists, ML engineers, and decision science analysts that support specific BUs/franchises.
• Set clear objectives, development plans, and ways of working that deepen both technical and business expertise in commercial decision science.
• Productize analytics and insights solutions appropriately to deploy frequently used applications at scale, champion knowledge sharing, code reuse, and best‑practice libraries so models and methods can be leveraged efficiently across brands and markets.

KNOWLEDGE, SKILLS & EXPERIENCE

Education

• Master’s or Ph.D. in Computer Science, Data Science, Statistics, Engineering, Mathematics, or a related quantitative field.

Experience

• 10+ years in AI/ML, data science, or advanced analytics; 5+ years in the US Pharmaceutical industry.
• 5+ years leading data science/ML teams in a matrixed, global or GCC/offshore environment.
• Experience working with commercial pharma data (e.g., IQVIA, Veeva, specialty pharmacy, claims, digital) and strong understanding of privacy/compliance requirements.

Technical skills

• Deep expertise in machine learning, NLP, recommendation systems, and personalization of use cases.
• Proficiency in Python (preferred), plus R/SQL/Spark, and experience with cloud‑based ML platforms (e.g., AWS, Azure, GCP).
• Hands‑on experience with MLOps practices, model deployment, monitoring, and production‑grade ML systems.
• Experience with Generative AI and LLM frameworks and their application to commercial pharma problems.
• Knowledge of experiment design, A/B testing, and causal inference techniques.

Leadership & business competencies

• Proven ability to translate business needs into a clear AI/ML roadmap and communicate complex concepts in simple, business‑relevant terms.
• Strong stakeholder management and collaboration skills, with experience working across functions and geographies.
• Track record of delivering innovative AI solutions while maintaining high standards for ethics, compliance, and quality.

TAKEDA LEADERSHIP BEHAVIORS

• Think Strategically — Demonstrate strategic enterprise thinking to find innovative ways to serve patients.
• Collaborate Cross-Functionally — Build diverse teams that deliver results while fostering inclusion.
• Drive Accountability — Execute with excellence and hold oneself and others accountable.
• Develop Talent — Attract, develop, and retain top talent, building capabilities for the future.

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