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GCM Evidence Generation Study Design & Execution Lead
Takeda
Bengaluru, IND
Full-time
Lead · 3+ years experience
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Job Description
Role Objectives:
As one of the first Statistics for Study Design and Execution Leads to join the Bengaluru GCM team, this role will establish the statistical capability required to support evidence generation study design and execution, covering statistical analysis plan development, programming, and real-world data analysis.
You will also provide hands-on statistical design and programming support across the evidence generation portfolio, enabling Global Evidence Generation study teams to focus on scientific leadership, endpoint strategy, and final study decisions while GCM delivers analytical execution, statistical planning documentation, and RWD methodology support.
Key Responsibilities:
Initial Capability Build
In the early build phase, work closely with Global Evidence Generation study leads and R&D statistical counterparts to document the scope, standards, tools, templates, and handoff protocols that will govern statistical support activities performed by the GCM going forward
Develop programming standards, analysis templates, SAP shells, output shells, and QC checklists to support consistent and reproducible statistical execution as the team scales to two Leads
Identify gaps in tool access, data system connectivity, or methodology guidance that need to be resolved before statistical activities can be fully transitioned to the GCM and flag these to the AD in a timely manner
Support with development of SOPs and onboarding materials for all statistical programming and RWD analysis activities
Statistical Analysis Planning & Study Design Support
Define appropriate statistical methods for RWD analyses and draft statistical analysis plans in collaboration with the subject matter expert to address evidence generation needs across the drug development life cycle
Lead the implementation of analytic plans, execute analyses for RWE studies using appropriate data sets, as well as standardized methods aligned with GxP for insights, publications, regulatory and payer submissions
Help design interventional and non-interventional (real world) studies, including (where appropriate) sample size calculations and probability of success
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