GCM Evidence Generation Study Design & Execution Lead

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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 and rationale in study files in line with repository and audit standards

Statistical Programming & Analysis Execution

• Generate analysis-ready datasets based on defined use cases and implement analytical methods producing guideline-compliant outputs including tables listings and figures (TLFs) with all data preparation steps documented and traceablethroughout

• Write validate and execute programs in SAS R or Python per the Statistical Analysis Plan (SAP); conduct exploratory and adhoc analyses as requested clearly documenting assumptions scope and limitations for each

• Support comparative effectiveness analyses subgroup analyses and health economic modeling by providing analyticalexecution and output production; identify and address common sources of bias in collaboration with the retained statisticalSME testing appropriately for effect modifiers and documenting methodology choices and rationale

• Develop and validate statistical models that appropriately account for intercurrent events competing risks and multiplecomparisons; conduct sensitivity and robustness analyses as appropriate flagging results and implications clearly for retainedscientific interpretation and reporting decisions

• Ensure all analytical steps are fully reproducible and auditable for regulatory HTA or other external review by maintainingcomplete programming logs data lineage records and version control across all programs datasets and outputs throughoutthe study lifecycle

Qualifications:

Experience: >3 years of related work experience; >2 years of experience in the biopharmaceutical industry; hands-on experience in biostatisticsstatistical programming or RWD analysis in a pharmaceutical CRO or HEOR context required; experience supporting study design and execution inan evidence generation clinical development or outcomes research setting strongly preferred

Technical skills:

• Knowledge of QC HTA-aligned analyses for payer/reimbursement evidence ( framework Network meta-analyses IPTW indirecttreatment comparisons).

• Knowledge of statistical analysis methods experimental design and data processing softwarefor both clinical trials and non-interventionalstudies

• Expertise inSAS R SQL Python epidemiology outcomes research US secondary data sources etc.

• Standards: CDISCADaM PICO HTA alignment

• Familiarity with and hands-on experience working on a variety of real-world data sources: Claims EHR/EMR registries observational databases.

• Experience with real-world dataset management

• Ability to QC and review other statisticians/analysts outputs

• Statistical programming of clinical and real-world data for Phase IV trials observational studies and RWE research

Education: Masters/PhD in public health health outcomes statistics or epidemiology; experience with Python SAS or R

Benefits