GCM Evidence Generation Study Design & Execution Lead

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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​

• 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 requiring retained 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 traceable throughout ​

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

• Support comparative effectiveness analyses, subgroup analyses, and health economic modeling by providing analytical execution and output production; identify and address common sources of bias in collaboration with the retained statistical SME, 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 multiple comparisons; conduct sensitivity and robustness analyses as appropriate, flagging results and implications clearly for retained scientific interpretation and reporting decisions ​

• Ensure all analytical steps are fully reproducible and auditable for regulatory, HTA, or other external review by maintaining complete programming logs, data lineage records, and version control across all programs, datasets, and outputs throughout the study lifecycle ​

Qualifications:

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

Technical skills:​

• Knowledge of QC HTA-aligned analyses for payer/reimbursement evidence (e.g. PICO framework, Network meta-analyses, IPTW indirect treatment comparisons).​

• Knowledge of statistical analysis methods, experimental design and data processing software for both clinical trials and non-interventional studies​

• Expertise in SAS, R, SQL, Python, epidemiology, outcomes research, US secondary data sources, etc.​

• Standards: CDISC, ADaM, 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: Master’s/PhD in public health, health outcomes, statistics, or epidemiology; experience with Python, SAS, or R​

Benefits

Locations

IND - Bengaluru

Worker Type

Employee

Worker Sub-Type

Regular

Time Type

Full time