GCM Evidence Generation Study Design & Execution Lead

Takeda; Bengaluru; On-site; INR 3,000,000 - 5,500,000; Full time

Job summary

Takeda in Bengaluru seeks a Statistics for Study Design and Execution Lead to establish the statistical capability for evidence generation study design and execution. You will provide hands-on statistical design, programming support, and RWD analysis across the portfolio, enabling study teams to focus on scientific leadership and final decisions while delivering analytical execution.

The role involves developing SAPs, analysis plans, and standardized methods aligned with GxP, with close

Responsibilities

• In early build, collaborate with study leads and R&D statisticians to document scope, standards, tools, templates, and handoff protocols.
• Develop programming standards, analysis templates, SAP shells, output shells, and QC checklists for reproducible execution.
• Identify gaps in tool access, data system connectivity, or methodology guidance and flag to the AD promptly.
• Support development of SOPs and onboarding materials for statistical programming and RWD analysis activities.
• Define statistical methods for RWD analyses and draft SAPs with the subject-matter expert.
• Lead the implementation of analytic plans and analyses for RWE studies using appropriate data sets and compliant methods.
• Help design interventional and non-interventional (real world) studies, including sample size calculations.
• Develop and update HTA CTAP, post-hoc analyses, and pre-specified analyses.
• Work on responder analyses using machine learning.
• Support external controls.
• Review draft study protocols and synopses for statistical consistency and flag methodological gaps or retained scientific review needs.
• Document all statistical planning decisions, assumptions.

Job description

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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 todocument the scope, standards, tools, templates, and handoff protocols that will govern statistical support activitiesperformed by the GCM going forward
• Develop programming standards, analysis templates, SAP shells, output shells, and QC checklists to support consistent andreproducible 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 statisticalactivities 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 subjectmatter 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 asstandardized methods aligned withGxPfor insights, publications, regulatory and payer submissions
• Help design interventional and non-interventional (real world) studies, including (where appropriate) sample size calculationsand probability of success
• Develop and update HTA CTAP (clinical trial analysis plan), post-hoc analysis, and pre-specified analyses
• Work on responder analyses using machine learning
• Support external controls
• Review draft study protocols and synopses for statistical consistency and flag methodological gaps or assumptions requiringretained scientific review and resolution
• Document all statistical planning decisions, assumptions,