Ranjeet Sharma

Ranjeet Sharma has built a career at the intersection of enterprise data analytics, statistical modeling systems, and clinical decision support, where the precision of analytics must align with the rigor of science and the demands of regulated healthcare environments. Across roles as Analyst, Program Manager, Project Lead, and Team Lead in pharmaceutical and healthcare analytics, his professional journey has been defined by an ability to translate highly complex clinical and research data into structured, compliant, and decision-ready systems. His work reflects a sustained commitment to building the analytical infrastructure that enables life sciences organizations to make faster, more reliable, and more scientifically grounded decisions.

At the core of Sharma’s technical profile is a strong foundation in statistical programming and data systems engineering. His expertise spans SAS and R, database technologies such as PostgreSQL and Redshift, business intelligence tools including Power BI and Tableau, and cloud environments across AWS and Microsoft Azure. This technical base is reinforced by peer-reviewed research in statistical methodology, including published work on reliability estimation for engineering systems in journals such as the International Journal of Quality & Reliability Management, the International Journal of Agricultural and Statistical Sciences, and the Journal of Applied Statistical Science. These scholarly contributions distinguish him not only as a practitioner of enterprise analytics, but also as someone who has contributed to the methodological foundations of the field.

Among Sharma’s most consequential professional achievements is his stewardship of the Clinical Outcomes Research Database (CORD) platform, developed on AWS and integrated into an enterprise web interface. As system custodian and assistant support lead, he helped maintain and advance a platform serving five therapeutic areas—Immunology, Oncology, Diabetes, Pain, and Neurodegeneration—across 65 clinical indications. The significance of this platform lies in its role as a curated external-data environment that enables scientists to make better-informed clinical design decisions. The data supported through CORD has been used for trial design simulations, dose-response modeling, Bayesian network meta-analyses, health technology assessments such as ICER and NICE, and post-launch comparative evaluations. In practical terms, Sharma’s work helped create an infrastructure through which large-scale clinical evidence could be transformed into usable scientific insight.

His leadership also extended into operational and organizational transformation. As Program Manager for the Simplicity Clinical Trial Management System (sCTMS), Sharma helped enable stronger data democratization for external company partners. In the FIPNET program, he integrated IT support for more than 400 partners, generating $7.5 million in contract value and savings while improving cross-organizational efficiency by 60%. He also led an Agile Enterprise transformation model that delivered $8 million in CAPEX savings through platform consolidation and digital strategy. These contributions show that his expertise is not limited to statistical modeling or system maintenance, but extends into the design of scalable, cost-effective, and strategically aligned enterprise analytics operations.

Sharma’s record further demonstrates strong performance in service management and operational excellence. Through oversight of change, incident, and problem management processes, he reduced SLAs by 70% and maintained 100% customer satisfaction for five consecutive years. Earlier, his oversight of seven Global Statistical Sciences applications for the Clinical Metadata Repository drove a 45% reduction in operational expenditure and produced $7.5 million in savings through automation and productivity improvements. These achievements reflect a professional who understands that in life sciences analytics, reliability and operational discipline are just as important as technical sophistication.

An additional example of Sharma’s practical innovation is his development of a Shiny application in R Studio to work around Power BI export limitations. By enabling the retrieval of millions of records in chunks from RDS PostgreSQL databases for statistical teams, this solution addressed a real operational bottleneck and improved the usability of enterprise data for analytical workflows. This type of contribution is emblematic of his broader approach: identifying structural limitations in enterprise systems and designing pragmatic, technically sound solutions that expand what those systems can accomplish.

What strengthens Sharma’s profile further is the breadth of his influence beyond direct technical delivery. His peer-reviewed publications place part of his work in the permanent scientific literature, while his contributions to models supporting regulatory submissions, payer economic analyses, and health technology assessments suggest that his work has influenced decisions affecting patient access to medicines across different markets. In highly regulated scientific domains, this kind of contribution carries significance beyond internal efficiency; it affects how evidence is interpreted, how therapies are evaluated, and ultimately how health decisions are made.

Throughout his career, Sharma has also treated knowledge transfer and mentorship as a core responsibility. His management of offshore-to-onshore transitions, facilitation of user story reviews and backlog refinement sessions, and alignment of Scrum teams all reflect an emphasis on cultivating stronger analytical teams and better organizational capability. His efforts in knowledge-sharing initiatives demonstrably improved team competencies and operational efficiency, showing that his impact extends into team development as well as system design.

For IICSPA Fellowship consideration, Ranjeet Sharma presents a compelling profile marked by statistical depth, enterprise analytics leadership, measurable operational and financial impact, scholarly contribution, and a sustained role in advancing clinical decision-support systems. His work reflects the level of technical maturity, practical significance, and professional leadership expected of a fellowship-level candidate.