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.
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Collaborate Cross-Functionally — Contribute positively to GCC-US team workflows and cross-functional collaboration.
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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.
Locations:
IND - Bengaluru
Worker Type:
Employee
Worker Sub-Type:
Regular
Time Type:
Full time

Work arrangement
No