Director, Computational Safety Assessment

Altos Labs•San Diego, CA

1d•$269,500 - $358,200

About The Position

An exciting opportunity is available for a talented Director to join the Institute of Medicine (IOM) Development Sciences team at Altos Labs. We are seeking a very highly motivated, curious, and collaborative Individual Contributor, who is passionate and dedicated to employing their expertise to restore cell health and resilience and reverse disease. This position will be part of a multidisciplinary team within the IOM to develop novel therapeutic projects and pipelines at Altos. You will work in a highly dynamic and cross-functional environment and bring a demonstrable track record of strong safety experience to drive data analysis and become an active participant of drug de-risking during discovery and translational research from target discovery to development.

Requirements

Demonstrable history of translating computational innovation, e.g., AI/ML methods, predictive modeling, multi-omics data analysis, and effective use of public/commercial data sources into tangible decisions and impacts in drug portfolio projects.
Strong track record of initiating, building, and scaling ideas across different functions and teams.
Proven ability to deliver scalable, high-impact insights and tools that shape decision-making.
Ability to stay current with scientific literature on targets and associated safety signals of interest and emerging computational methodologies, applying it to our work and driving innovation across the company
Can demonstrate a natural curiosity to explore and question ideas and data to drive innovative approaches, learning and continuous improvements.
Respected contributor and ability to influence with positive impact to strategic conversations and ideas, even without a formal team reporting line.
Able to give and receive constructive and clear feedback to peers, leaders, collaborators in a candid manner, enabling trusting working relationships
Experience managing budgets, outsourcing, and cross-site operations.
Ph.D. degree in Computational Biology/Chemistry/Toxicology, Bioinformatics, Computer Science, Data Science, or a related scientific field required.
Fifteen-plus (10+) years of experience in computational drug safety in a pharmaceutical company, with deep knowledge of toxicology, pharmacology, and the role of computation in the drug discovery and development process.
Five-plus (5+) years of experience in strategic leadership, global team management, and cross-functional influence.

Responsibilities

Provide strategic leadership in defining and implementing the computational biology strategy within IoM, in alignment with broader computational innovation goals at Altos Labs.
Provide long-term vision and oversee cross-functional integration with Research, Clinical Pharmacology, Safety, and IT. This role will be supported by and provide matrix leadership, as an Individual Contributor, to a team, comprising in-house expertise and specialized external partners.
Collaborate closely with Discovery Sciences, Computational Biology, Functional Genomics, Chemistry and Proteomic colleagues across Altos to share expertise, best practices and data analysis to support projects and goals.
Lead the design and analysis of functional gene editing screens and multi-omics datasets such as DNA sequencing, Transcriptomics and Epigenomics
Lead biomarker initiatives through the integrative analysis of internal and external genomic/genetic data
Support target discovery and translational research through the development of methods to interrogate large OMICs data sets
Build and maintain computational pipelines for the integrative analysis and visualization of Genomics and Proteomics data to support target liability assessment and guide clinical monitoring strategies
Oversee data infrastructure and computational environment to ensure robust data management
Communicate data analysis and scientific strategy effectively to internal stakeholders and external partners
Support development of new approach methodologies (NAMs) and computational methods to achieve 3Rs in drug development.
Understand specific business needs and translate the scientific and regulatory challenges and opportunities into smarter data innovation solutions, and drive validation strategies for model performance.
Drive rollout, re-enforcement, monitoring, and continuous improvement of implemented data science initiatives.
Mentor and provide training on the use and utility of predictive ML approaches in drug safety and submissions.