Senior Manager, Data Modeling

Takeda•Boston, MA

23h•Onsite

About The Position

As a Senior Manager, Data Modeling, you will provide hands-on data modeling expertise to deliver high-quality, analysis-ready data assets in support of clinical operations and regulatory functions within R&D. You will lead defined initiatives and workstreams, ensures high-quality execution, and mentors team members while partnering closely with clinical, regulatory, and data stakeholders.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, Information Technology, or related field.
4–6 years of experience in data modeling, data architecture, and/or data management in complex environments.
Experience contributing to analytics or AI ready data initiatives.
Advanced data modeling and data architecture skills
Strong data harmonization and curation experience.
Working knowledge of modern data platforms and cloud environments.
Strong working knowledge of clinical and regulatory domains.
Ability to lead initiatives and influence cross-functional teams without direct authority.
Strong focus on accuracy and the ability to identify and resolve issues.
Ability to identify challenges and propose innovative solutions related to optimization needs
Ability to work effectively in a collaborative environment across multiple teams and departments.

Nice To Haves

Master’s degree in Data Science, Information Management, or related field is preferred.
Experience supporting clinical operations and/or regulatory data preferred.
Life sciences or pharmaceutical domain experience.
Familiarity with regulatory data standards and compliance requirements.

Responsibilities

Lead the design and implementation of data models for assigned clinical operations and regulatory initiatives.
Develop and deliver data mapping and harmonization specifications aligned with established modeling standards.
Act as a hands on subject matter expert, owning model design quality through implementation.
Partner with clinical operations and regulatory stakeholders to identify and prepare priority datasets for analytics and AI use cases.
Design and implement data curation approaches within defined scope, in collaboration with Data Organization SMEs.
Ensure curated datasets are fit for purpose, well documented, and consumable by analytics and data science teams.
Design and manage logical and physical data structures supporting clinical trial operations, oversight, and regulatory needs.
Contribute to architecture decisions within assigned initiatives, ensuring alignment with established platforms and patterns.
Support the improvement of data centric processes that enhance data quality, traceability, and inspection readiness.
Collaborate with clinical and regulatory teams to identify data related gaps and implement practical, executable improvements.
Apply established best practices and standards to ensure consistent delivery across initiatives.
Design and maintain semantic data models supporting prioritized clinical and regulatory use cases.
Document models clearly for reuse and governance adherence.
Work with Data Governance partners to ensure data assets adhere to onboarding, quality, and stewardship expectations.
Own the ongoing enhancement and maintenance of assigned data models.
Contribute to improvements of shared or existing models under defined guidance.
Serve as a technical lead and day to day mentor for data modelers within assigned initiatives.
Collaborate closely with peers across Data Modeling, Governance, Analytics, and R&D functions to deliver integrated solutions.
Deep hands on contribution to data modeling and harmonization work.
Accountable for quality, timeliness, and usability of delivered models.
Coaches and supports team members through work based mentorship.
Leads delivery within defined scope.
Make sound design and implementation decisions within assigned scope.
Escalate and align when decisions exceed role authority.
Manage complex datasets and integrations across multiple systems.
Balance delivery needs with quality and compliance requirements.
Work effectively across Data Modeling, Governance, Analytics, and R&D partner teams.
Understand downstream impacts from data source to insight generation.