Principal Analyst Decision Science and AI Enablement

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Job Description

OBJECTIVES / PURPOSE

We are seeking a high-caliber Principal Analyst to join Takeda's GCC Commercial Analytics & Insights (CA&I) organization in India as part of the Decision Science & AI Enablement COE. This senior individual contributor role serves as a technical and consultative anchor for complex AI/ML, advanced analytics, decision science, and GenAI workstreams supporting US and global commercial priorities.

ACCOUNTABILITIES

AI/ML Strategy and Delivery

·       Lead complex model development, validation, monitoring, and lifecycle management workstreams across classification, regression, NLP, recommendation, deep learning, and personalization use cases.

·       Frame ambiguous commercial business problems into structured analytical approaches, identifying data requirements, methodological options, success metrics, and implementation considerations.

·       Guide integration of claims, CRM, digital engagement, EMR, specialty pharmacy, omnichannel, and other commercial behavioral datasets into scalable AI/ML solutions.

·       Produce executive-ready technical narratives that explain methodology, performance, limitations, business implications, and recommendations for model adoption or refinement.

Personalization and Decision Frameworks

·       Architect reusable decisioning frameworks for next-best-action / next-best-channel, patient identification, HCP targeting, segmentation, and engagement prioritization.

·       Design experimentation and measurement approaches, including A/B testing, control groups, uplift analyses, and KPI frameworks to quantify business impact.

·       Partner with DD&T, Omnichannel, Marketing Operations, and analytics teams to operationalize model outputs into business workflows while preserving quality and traceability.

Innovation and GenAI

·       Lead evaluation and prototyping of GenAI and LLM-based solutions for insight synthesis, content support, literature and knowledge retrieval, and intelligent assistants for analytics teams.

·       Define evaluation criteria, quality gates, and documentation standards for GenAI pilots so outputs are transparent, reliable, and aligned with approved guardrails.

·       Convert successful prototypes into reusable analytical assets, prompts, code patterns, and implementation playbooks that can be leveraged across brands and markets.

COE Excellence and Methodology Standards

·       Provide technical guidance, code review, model review, and methodology coaching to Senior Analysts and Analysts across assigned workstreams.

·       Establish and maintain best-practice libraries, reusable modeling templates, validation checklists, and documentation standards for the Decision Science & AI Enablement COE.

·       Contribute to COE capability building by sharing emerging methods, automation opportunities, and practical applications of AI/ML and GenAI in commercial pharma analytics.

KNOWLEDGE, SKILLS & EXPERIENCE

Education:

·       Bachelor's or master's degree required in Computer Science, Data Science, Statistics, Engineering, Mathematics, Operations Research, or a related quantitative field.

·       Advanced degree in Data Science, Statistics, Computer Science, Engineering, or a related quantitative field is strongly preferred.

Experience:

·       5–6 years of progressive experience in AI/ML, data science, advanced analytics or predictive analytics in the pharmaceutical space

·       Demonstrated experience independently leading complex model development, decision science, experimentation, or personalization workstreams.

·       Advanced proficiency in Python, SQL, and applied machine learning methods; working knowledge of Spark/PySpark, Databricks, and cloud-based ML platforms is preferred.

·       Applied experience with commercial pharma or healthcare datasets such as claims, CRM, digital engagement, EMR, specialty pharmacy, omnichannel interaction, or sales data.

·       Experience with model deployment, monitoring, documentation, and lifecycle management practices is strongly preferred.

·       Experience partnering with commercial, omnichannel, DD&T, or AI/ML engineering stakeholders in a matrixed environment is preferred.

Skills & Competencies:

·       Advanced AI/ML Expertise — Designs and guides complex analytical methodologies across predictive modeling, NLP, recommendation systems, personalization, and experimentation.

·       Consultative Partnership — Frames business questions, recommends analytical approaches, and influences stakeholders through clear technical and commercial reasoning.

·       Technical Proficiency — Advanced Python and SQL; strong understanding of ML frameworks, Databricks, Spark/PySpark, cloud ML platforms, and model monitoring practices.

·       Decision Science Leadership — Builds reusable decisioning frameworks that connect model outputs to commercial workflows and measurable business outcomes.

·       GenAI Enablement — Evaluates LLM-based solutions, defines quality standards, and translates prototypes into reusable assets under approved guardrails.

·       Data Storytelling — Synthesizes complex model results into concise, decision-oriented narratives for senior technical and business audiences.

·       Informal Leadership — Provides technical mentorship, methodology review, and best-practice guidance across the COE.

TAKEDA BEHAVIORS

In alignment with Takeda's Values-Based Culture, this role requires demonstration of the following leadership behaviors:

• Act with Integrity — Deliver accurate, transparent work and take full responsibility for quality.
• ;">Act with Integrity — Deliver accurate, transparent work and take full responsibility for quality.;">Act with Integrity — Deliver accurate, transparent work and take full responsibility for quality.
• Collaborate Cross-Functionally — Contribute positively to GCC-US team workflows and cross-functional collaboration.
• ;">Collaborate Cross-Functionally — Contribute positively to GCC-US team workflows and cross-functional collaboration.;">Collaborate Cross-Functionally — Contribute positively to GCC-US team workflows and cross-functional collaboration.
• Drive Accountability — Take ownership of all assigned tasks and deliver on commitments.
• ;">Drive Accountability — Take ownership of all assigned tasks and deliver on commitments.;">Drive Accountability — Take ownership of all assigned tasks and deliver on commitments.
• Embrace Learning — Continuously build analytical and domain capabilities through feedback and self-driven development.
• ;">Embrace Learning — Continuously build analytical and domain capabilities through feedback and self-driven development.;">Embrace Learning — Continuously build analytical and domain capabilities through feedback and self-driven development.

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