Senior Principal, Scientist, Computational Sa

Description We are seeking a Senior Principal Scientist, Computational Safety, to provide scientific leadership in in silico safety assessment across drug discovery and development. This role will leverage advanced multi‑omics data, systems toxicology and AI/ML approaches to proactively identify safety liabilities, support mechanistic understanding, improve quality target identification, and facilitate risk‑based decision‑making. The Senior Principal Scientist will play a key role in developing and delivering integrated computational safety strategies, including AI‑enabled foundation model based on multiple omics data, Adverse Outcome Pathways, off-target identification, and predictive liability models. Working closely with toxicologists, discovery scientists, clinical safety, bioinformatics, data science, and IT partners, this position will translate complex computational insights into clear, actionable recommendations that improve pipeline quality and reduce late‑stage attrition. As an employee of Boehringer Ingelheim, you will actively contribute to the discovery, development and delivery of our products to our patients and customers. Our global presence provides opportunities for all employees to collaborate internationally, offering visibility and opportunity to directly contribute to the companies' success. We realize that our strength and competitive advantage lie with our people. We support our employees in a number of ways to foster a healthy working environment, meaningful work, mobility, networking and work-life balance. Our competitive compensation and benefit programs reflect Boehringer Ingelheim's high regard for our employees. Duties & Responsibilities

• Lead in silico target safety assessment strategy development
• Develop and deliver integrated computational safety packages to support program and portfolio decisions
• Advance AI/ML and multi omics-based models, including foundation models, for safety and liability prediction
• Drive AI enabled Adverse Outcome Pathway (AoP) development to support mechanistic risk assessment
• Proactively identify and communicate on target and off target safety risks
• Collaborate closely with Toxicology, Discovery, Clinical Safety, Bioinformatics, Data Science, and IT teams
• Serve as a bridge and clearly communicate complex computational insights to multidisciplinary stakeholders. Requirements
• Ph.D. in Computational Toxicology, Computational Biology, Bioinformatics, Molecular Biology, or related field with minimum 7+ years of post doctoral and/or industry experience in drug discovery and development and ≥3 years of experience in safety risk assessment is a must
• Expertise in computational toxicology, AI/ML modeling, and multi omics (TGx) data integration
• Experience with in silico safety, liability prediction, and mechanistic modeling
• Familiar with toxicology related data curation, and interpretation, and knowledge of GLP toxicology and regulatory guidance will be a favorable plus
• Strong collaboration, scientific leadership, and communication skills, with the ability to translate complex computational results into actionable insights for stakeholders with diverse backgrounds Eligibility Requirements
• Must be legally authorized to work in the United States without restriction.
• Must be willing to take a drug test and post-offer physical (if required)
• Must be 18 years of age or older @computationalinnovation Compensation Data This position offers a base salary typically between $140,000 and $222,000.

The position may be eligible for a role specific variable or performance-based bonus and or other compensation elements. For an overview of our benefits please click here. (https://www.boehringer-ingelheim.com/us/careers/benefits-rewards) All qualified applicants will receive consideration for employment without regard to a person's actual or perceived race, including natural hairstyles, hair texture and protective hairstyles; color; creed; religion; national origin; age; ancestry; citizenship status, marital status; gender, gender identity or expression; sexual orientation, mental, physical or intellectual disability, veteran status; pregnancy, childbirth or related medical condition; genetic information (including the refusal to submit to genetic testing) or any other class or characteristic protected by applicable law.