Top Biotech Data Analysis Jobs in Boston, MA

The Director, Statistical Programming leads statistical analysis for clinical studies, oversees programming resources, and ensures compliance with regulatory standards. They mentor programmers and drive technological advancements in data analysis.

The Senior Principal Analyst leads statistical programming activities, oversees programming standards, and ensures compliance with submission requirements while managing project timelines and resources for clinical trials.

The Senior/Principal Scientist will lead computational analyses of large-scale omics data to support drug discovery, develop scalable analytical workflows, and collaborate with cross-functional teams for actionable insights.

Lead statistical efforts in clinical programs, ensuring compliance with regulations, contributing to clinical protocols, and managing CRO relationships. Oversee statistical outputs and guide team members.

The Statistical Programmer will lead programming support for clinical data analysis, ensuring compliance, managing CROs, and developing automation tools. Requires SAS expertise.

The Principal Biostatistician manages statistical activities for clinical trials, develops strategies, oversees deliverables, and ensures compliance with regulatory standards, while collaborating with teams to support drug development.

The Sr. Manager/AD, Statistical Programmer will lead statistical programming for clinical studies, manage CROs, and ensure successful data analysis for regulatory submissions. Responsibilities include overseeing dataset creation, collaborating with clinical teams, and mentoring programming staff.

The Bioinformatics Scientist analyzes lab data through computational models, focusing on NGS data and collaborating with teams to develop software solutions.

The Supply Chain Data Scientist develops analytics to enhance supply chain efficiency and reliability, working with operational data to optimize network performance and decision making.

Lead RWD analysis strategy and execution: develop technical specifications and statistical programs, perform complex observational analyses (propensity scoring, ML), validate cohorts/variables, ensure QC, collaborate cross-functionally, and communicate results to non-technical stakeholders.