About Us

Kardigan is a heart health company working to make cardiovascular disease preventable, curable and no longer the leading cause of death in the world.

It is Kardigan’s mission to develop multiple targeted treatments in parallel that bring people with cardiovascular diseases to the cures they deserve.

Led by Tassos Gianakakos, Jay Edelberg, M.D., Ph.D., and Bob McDowell, Ph.D., Kardigan’s co-founders have reunited after leading MyoKardia to discover and develop mavacamten, the first cardiac myosin inhibitor, resulting in an acquisition by Bristol Myers Squibb in 2020.

We have a cutting-edge discovery and translational research platform, a pipeline of late-stage candidates, and an industry-leading team that is driven to improve the lives of patients.

At Kardigan, we are motivated by our values which guide how we work, interact, and achieve our goals. Driven by patients and their families, we are deeply committed to improving the lives of patients and prioritizing their needs above all else. We believe in being authentic—leading with truth to bring out the best in others by creating an environment where every person knows they will be fully accepted. With an eagerness to learn, we encourage the highest levels of curiosity and are open to changing our minds. We are committed to winning as a team with urgency, excellence, and intention, and support each other no matter what role we play or where we sit. Lastly, we strive to enable the impossible because patients are counting on us. We are not afraid to take risks to unlock innovation and advance scientific discoveries.

These values are the foundation of our work, empowering us to make a real difference, every day.

Department: Clinical Data Sciences & Analytics

Reports To: Head of Statistical Programming

Location: South San Francisco, CA or Princeton, NJ – On-site 4 days per week (Mon to Thurs)

Job Overview:

The Associate Director of Statistical Programming will serve as the compound lead, responsible for programming strategy and delivery across all studies and integrated analyses for an assigned compound. This role will lead internal and outsourced programming activities, ensuring quality, consistency, and timely delivery of clinical reporting and regulatory submission deliverables, while remaining hands-on in SAS/R programming and technical review.

This position will serve as the primary Statistical Programming representative on compound teams, partnering with Biostatistics and Clinical Data Management (CDM) to align data collection, analysis requirements, and programming plans. The compound lead will establish consistent programming standards across studies, coordinate resources and timelines, and proactively address delivery risks. Additionally, this position will contribute to establishing SOPs and programming standards and modernizing statistical reporting with open-source tools.

ESSENTIAL DUTIES AND RESPONSIBILITIES

Key Project Responsibilities:

Serve as the compound programming lead, accountable for study- and compound-level programming deliverables in collaboration with the lead biostatistician and cross-functional teams.
Develop and maintain programming plans, coordinate timelines and resource needs across studies, and proactively address delivery risks, escalating resource constraints and unresolved issues to the Head of Statistical Programming.
Lead programming review of SAPs, Data Presentation Plans, and Table Shells across the compound to align with development objectives and ensure clarity, consistency, and completeness of programming assumptions and requirements.
Lead the programming team to produce and/or validate SDTM, ADaM, and TLF deliverables across studies and integrated analyses for CSRs, regulatory requests, publication needs, and ad hoc analyses, remaining hands-on for complex programming and technical review.
Manage CROs and contractors supporting the compound, defining scope and acceptance criteria and ensuring compliance, quality of deliverables, and timelines.
Collaborate with biostatisticians and clinical development colleagues to develop/review TLF shells, SDTM, and ADaM specifications, establishing consistent data standards and derivation conventions across studies to support compound-level analyses.