MSAT AI Lead

Takeda; Hinoba-an; On-site; PHP 2,984,000 - 4,974,000; Full time

Job summary

Takeda in Bengaluru seeks an MSAT AI Lead to operationalize AI strategy across MSAT modalities. You will develop AI-enabled data products in Databricks, govern models, and drive agile AI delivery in a regulated pharma environment.

Ideal candidates have 8–10 years in pharma/biotech with MSAT/CMC exposure, strong Databricks skills, and a track record delivering AI data products within SAFe. Global collaboration and digital adoption are core expectations.

Qualifications

• Advanced degree (Master’s or higher) in STEM, Computer Science, Data Science, or related field.
• 8–10 years of experience in the pharmaceutical or biotech industry, with MSAT/CMC exposure.
• Hands-on Databricks experience and SAFe delivery in regulated environments.
• Proven track record delivering AI data products and governing AI initiatives.

Responsibilities

• Operationalize the MSAT AI strategy and translate requirements into scalable solutions.
• Develop and deploy AI-enabled data products and agentic workflows.
• Govern AI solutions with model risk, explainability, and compliance practices.
• Lead cross-functional collaboration and drive AI adoption across MSAT modalities.

Skills

Databricks

SAFe

AI product delivery

GxP compliance

Stakeholder leadership

Education

Master's degree in STEM

Job description

Job Description

ROLE: MSAT AI LEAD

LOCATION : BENGALURU

OBJECTIVE:

• Operationalize the MSAT AI strategy, translating scientific and operational requirements into scaled solutions within Takeda’s EDB (enterprise data backbone)
• Develop agentic AI solutions within Databricks and any other GSQ approved NorthStar platform
• Manage, govern MSAT agentic AI solutions in alignment with SAFe principles & agile delivery practices
• Create measurable business value through process optimization, MSAT workflow automation, agentic troubleshooting, knowledge management, and accelerated decision-making in GSQ MSAT
• Monitor digital adoption and foster community of practices for agentic AI in MSAT

ACCOUNTABILITIES:

• Define and execute the MSAT AI roadmap aligned with business priorities of all modalities.
• Develop & deploy AI-enabled data and process knowledge[DK1]products including, but not exhaustive, predictive process monitoring, CPV intelligence, yield optimization, process capability analytics, and digital copilots.
• Partner with MSAT, Manufacturing, Quality, ICC, and DD&T teams to identify high-value AI use cases.
• Establish AI product lifecycle management from ideation and experimentation through deployment, validation, monitoring, and continuous improvement.
• Lead AI governance including model risk management, explainability, compliance, and responsible AI practices.
• Support the standardization of data models, ontologies, taxonomies required for AI scalability.
• Champion digital adoption through training, coaching, and creation of AI communities of practice.
• Scout and evaluate emerging AI technologies, agent platforms and orchestration [DK2], model capabilities, digital twin integrations, and automation patterns; build business cases and pilots that demonstrate tangible MSAT value and scalability

DIMENSIONS AND ASPECTS:

Technical/Functional (Line) Expertise:

• Strong understanding of the end-to-end product and process lifecycle, spanning development, technology transfer, commercial manufacturing, CPV, troubleshooting, and lifecycle management.
• Deep expertise in MSAT, process engineering, manufacturing sciences, process knowledge management, and how data and knowledge are generated, contextualized, transferred, and reused across modalities and sites.
• Strong understanding of AI in Databricks, generative AI, agentic workflows, prompt engineering, model evaluation, AI orchestration, retrieval-augmented generation, data integration, and workflow automation in regulated environments.
• Solid knowledge of MSAT and manufacturing digital architecture, including NorthStar, PLM, ELN, MES, Discoverant, data platforms such as Databricks, digital twin environments, and enterprise integration patterns.
• Strong familiarity with GxP expectations, data integrity, validation/qualification approaches, regulatory inspection readiness, cybersecurity, privacy, and responsible AI principles.

Leadership:

• Serve as the MSAT AI thought leader and trusted advisor.
• Influence senior stakeholders and align cross-functional teams around AI-driven transformation.
• Mentor scientists, engineers, and product owners in AI best practices and data-driven decision making.
• Promote a culture where knowledge and insights are accessible through AI-enabled tools.

Decision-making and Autonomy:

• Makes informed trade-off decisions balancing business value, scientific rigor, compliance, user experience, enterprise alignment, technical feasibility, supportability, and lifecycle impact.
• Operates with a high degree of autonomy within established governance, escalating decisions with clear options, rationale, risks, and quantified business impact when needed.
• Resolves cross-functional barriers related to data access, architecture, regulatory expectations, AI risk, adoption, and operating model maturity.

Interaction:

• Partner with MSAT Process Knowledge & Network Lead in deployment of AI solutions [DK1]
• Build and strengthen data & process knowledge relationships across MSAT modalities and partner functions (R&D Pharmaceutical Sciences) to align on global standards and shared data products
• Interface with DD&T ICC, CMC and Process Science across modalities to define business requirements, data requirements, governance requirements

Innovation:

• Anticipate and respond to shifts in technology and industry trends to position Takeda at the forefront of digital and model-based manufacturing.

Complexity:

• Collaborate across multiple NorthStar tools and platforms to enable integrated data solutions
• Balance global standards with local business and operational needs
• Adapt effectively to evolving CMC data landscapes and changing business requirements

EDUCATION, BEHAVIOURAL COMPETENCIES AND SKILLS:

• Advanced degree (Master’s or higher) in STEM, Computer Science, Data Science, or a related field
• Minimum of 8 - 10 years of experience in the pharmaceutical or biotech industry, with expertise in CMC and/or MSAT
• Strong hands-on experience with Databricks or similar
• Proven track record of delivering AI data products using the SAFe (Scaled Agile Framework) methodology
• Extensive experience managing and maintaining data products within GxP regulated environments
• Demonstrated ability to deliver impactful solutions and drive collaboration within complex matrix organizations

Locations

IND - Bengaluru

Worker Type

Employee

Worker Sub-Type

Regular

Time Type

Full time