Delivery Manager- Life Sciences

We are seeking an experienced 18+ years - Delivery Manager with deep expertise in the Life Sciences (Pharma, Biotech) domain to lead the end-to-end delivery of IT projects, ensuring alignment with business goals, compliance, and innovation. The ideal candidate will manage cross-functional teams, oversee Agile/Waterfall delivery, and drive digital transformation in areas like R&D, Clinical Trials, Regulatory, Manufacturing, or Commercial Operations. Key Responsibilities -Project Delivery Leadership: Lead the planning, execution, and delivery of IT projects (e.g., SaaS, ERP, Data Analytics, Cloud, LIMS, CTMS, QMS) within the Life Sciences domain. Ensure projects meet scope, timeline, budget, and quality standards (GxP, FDA, HIPAA, GDPR compliance as needed). -Stakeholder & Vendor Management: Collaborate with business stakeholders (R&D, Manufacturing, QA, Commercial) to translate requirements into IT solutions. Manage third-party vendors, SaaS providers, and offshore teams. -Domain Expertise: Apply knowledge of Life Sciences processes (e.g., Clinical Development, Regulatory Submissions, Serialization, Supply Chain, Pharmacovigilance). Stay updated on industry trends (e.g., Digital Health, AI/ML in Drug Discovery, Real-World Evidence). -Risk & Compliance: Mitigate risks and ensure adherence to regulatory standards (21 CFR Part 11, ISO 13485, ICH-GCP). Drive validation (IQ/OQ/PQ) for regulated systems. -Team Leadership: Mentor teams (developers, analysts, QA) and foster Agile/DevOps practices. Optimize delivery processes using tools like JIRA, ServiceNow, or Azure DevOps. Qualifications Hands-on experience with systems like Veeva, Salesforce Health Cloud, SAP S/4HANA, LabVantage, or Medidata Rave. Certifications: PMP, CSM, SAFe, or ITIL preferred. Skills: Strong understanding of SDLC, Agile, and compliance frameworks. Excellent communication and stakeholder management. Preferred Attributes Experience with AI/ML, IoT, or Blockchain in Life Sciences. Knowledge of emerging tech (e.g., Federated Learning for Clinical Trials)

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