Statistical Programmer (SAS/R) – Clinical Trials
Mumbai / Pune / Bangalore / Noida | General Shift | ₹9–20 LPA | 

About the Role
Turn data into life-changing therapies. As a Statistical Programmer, you’ll lead programming activities for an entire compound or therapeutic area, transforming raw clinical data into the rigorous analyses that drive regulatory approvals and ultimately improve patient lives. You’ll own the creation of ADaM datasets, tables, listings, and figures, mentor a team of talented programmers, and collaborate with statisticians, data managers, and medical writers across the full clinical study lifecycle. This role offers the chance to shape standards, influence submissions, and grow your leadership within a supportive, innovation-focused environment that values scientific curiosity and technical excellence.

What You’ll Do

  Lead Programming Strategy: Own statistical programming for a compound or indication, ensuring all deliverables meet timelines, quality standards, and CDISC compliance.

Build and Validate ADaM Datasets: Develop and oversee SAS programs to create analysis-ready datasets, maintaining consistency across individual studies and integrated data.

Generate Critical Outputs: Create and validate tables, listings, and figures (TFLs) that form the core of clinical study reports and regulatory submissions.

Drive Regulatory Excellence: Prepare documentation for filings, including reviewer’s guides and data definition documents, and serve as the programming representative in cross-functional teams (statisticians, clinical data management, medical writing, regulatory publishing, clinical operations).

Mentor and Manage Resources: Lead a team of statistical programmers and analysts, plan resources, provide oversight, and foster growth through mentoring and standard-setting.

Develop Standard Tools: Create and maintain SAS macros, contribute to SOPs, and identify process improvements that boost efficiency and quality across projects.

Adapt and Communicate: Thrive in a dynamic environment, accurately estimate project effort, and communicate complex programming concepts to both technical and non-technical audiences.

What You’ll Bring

Experience & Education: A life science, computer science, or statistics graduate with a minimum of 2+ years of hands-on SAS programming in the drug development or clinical research space. Prior experience leading a team of programmers is essential.

Technical Mastery: Deep proficiency in SAS programming for ADaM, TFLs, and statistical analyses, with a strong understanding of CDISC standards (SDTM, ADaM). Exposure to R programming is highly valued.

Therapeutic Knowledge: Familiarity with oncology, immunology, neuroscience, or similar areas, and a track record of contributing to regulatory filings.

Leadership & Collaboration: Proven ability to represent the programming function in multidisciplinary teams, manage resources, and mentor junior colleagues with clarity and empathy.

Mindset: An analytical problem-solver who embraces changing priorities, communicates effectively in English (verbal and written), and maintains unshakeable attention to detail even under pressure.

Eligibility

Graduation is mandatory.

Role level (BPO 3–6) will be aligned to your experience and interview performance.

Why Join Us
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You’ll become part of a global life-sciences powerhouse that combines technology and domain expertise to accelerate the development of new medicines. Here, your programming skills directly influence trial outcomes, regulatory success, and patient access. We invest in your growth through continuous learning, cross-functional exposure, and a culture that celebrates innovation, diversity, and work-life harmony. Enjoy a competitive salary (₹9–20 LPA) that reflects your impact, alongside comprehensive benefits and a clear path to senior leadership.

How to Apply
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Are you ready to program the future of healthcare?  Please note that PAN and date of birth will be required for profile creation upon selection. We look forward to meeting detail-driven leaders who want to make a statistical difference