Associate Director, Statistical Programming
Company: Novartis
Location: Hyderabad, India (Office-based)
Job Type: Full-time | Permanent
Experience Required: Minimum 10 years in statistical programming (SAS expertise required)
Job Overview
Novartis is seeking an experienced Associate Director, Statistical Programming to lead statistical programming strategy, analytics development, and data reporting across clinical drug development programs. This senior-level role is responsible for overseeing programming deliverables across multiple studies, ensuring compliance with regulatory standards, and driving high-quality, timely outputs for clinical trials.
The role involves leadership of programming teams, cross-functional collaboration, and strategic contribution to clinical development and regulatory submissions.
Key Responsibilities
Statistical Programming Leadership
Lead statistical programming activities across multiple clinical studies or disease areas
Define and implement programming strategies at study and project level
Ensure consistency and standardization of programming deliverables across projects
Provide expert-level programming solutions for complex clinical data challenges
Clinical Data & Analytics Delivery
Oversee development and validation of datasets, analysis outputs, and reports
Ensure accuracy and quality of deliverables for regulatory submissions, safety reports, and publications
Support post-marketing and clinical research analytics activities
Act as subject matter expert for problem-solving in statistical programming
Team & Stakeholder Management
Function as a functional manager for local programming teams, providing guidance and supervision
Collaborate with cross-functional teams including clinical operations, biostatistics, and data management
Communicate project status, timelines, risks, and resource planning to stakeholders
Mentor and develop junior programming professionals
Regulatory & Compliance Support
Ensure adherence to CDISC standards and regulatory expectations
Support development and review of key clinical study documents including protocols, SAPs, eCRFs, and data specifications
Ensure programming deliverables meet audit and inspection readiness requirements
Report technical issues or adverse events in compliance with regulatory timelines
Innovation & Continuous Improvement
Contribute to innovative programming approaches and process improvements
Explore and adopt new technologies such as R, AI, and machine learning tools
Promote knowledge sharing and best practices across teams
Required Skills & Expertise
Strong expertise in SAS programming for clinical trial data analysis
Deep understanding of clinical trial data standards (CDISC)
Experience in end-to-end study programming responsibilities
Knowledge of clinical development documents (protocols, SAPs, eCRFs, mock shells)
Exposure to or willingness to adopt R programming and emerging technologies (AI/ML)
Strong analytical, problem-solving, and leadership skills
Excellent communication and stakeholder management abilities
Educational Requirements
Bachelor’s degree in Statistics, Life Sciences, Computer Science, or related field is required
Master’s degree (MSc or equivalent) is preferred
Experience Requirements
Minimum 10 years of experience in statistical programming, preferably within pharmaceutical, biotechnology, or clinical research organizations
Strong hands-on experience in SAS programming for clinical trials is mandatory
Experience leading programming teams or acting as study/program lead is required
Exposure to regulatory submissions and global clinical development environments is preferred
Key Focus Areas
Clinical Statistical Programming
Clinical Data Analytics
CDISC Standards Implementation
Regulatory Submissions Support
Clinical Trial Data Management & Reporting
About the Organization
Novartis is a global leader in healthcare innovation, focused on transforming clinical development through advanced analytics, data science, and cutting-edge technology. The organization is committed to improving patient outcomes through science-driven and data-enabled decision-making across the drug development lifecycle.
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