Principal Analyst Decision Science and AI Enablement

Job Title: Principal Analyst Decision Science and AI Enablement Company: Takeda Job Category: Data Science Roles & Responsibilities: 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. 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. 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. Work Location: Bengaluru, Karnataka, India Availability: Full time

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. Key Skills: Data Science Salary: Not disclosed