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.

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

Review draft study protocols and synopses for statistical consistency and flag methodological gaps or assumptions requiring retained scientific review and resolution

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

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

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

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.

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

Master's/PhD in public health, health outcomes, statistics, or epidemiology; experience with Python, SAS, or R

It is our priority to provide competitive compensation and a benefit package that bridges your personal life with your professional career. Competitive Salary + Performance Annual Bonus

- Adaptable work environment, including hybrid working
- Comprehensive Healthcare Insurance Plans for self, spouse, and children
- Group Term Life Insurance and Group Accident Insurance programs
- Health & Wellness programs including annual health screening, weekly health sessions for employees.
- Employee Assistance Program
- 5 days of leave every year for Voluntary Service in addition to Humanitarian Leaves
- Diversity, Equity, and Inclusion Programs
- Reimbursements – Home Internet & Mobile Phone
- Employee Referral Program
- Leaves – Paternity Leave (4 Weeks) , Maternity Leave (up to 26 weeks), Bereavement Leave (5 days)

Takeda is leading a digital revolution. As an organization, we are committed to our cloud-driven business transformation and believe the ICCs are the catalysts of change for our global organization.

Full time

Full time

Full time

Full time

📌 Design Management Lead (Bengaluru)
🏢 Takeda
📍 Bengaluru