Director, Biopharma Data Science

About This Opportunity

We are representing a confidential AI biotech organization at the forefront of computational drug discovery. Our client is building advanced agentic AI systems to accelerate pharmaceutical research and therapeutic development. They are seeking a Director of Biopharma Data Science to lead a team of scientists building and applying AI-powered discovery platforms to real-world drug development challenges.

This is a rare opportunity to shape how autonomous AI systems transform target discovery, biomarker identification, and disease understanding. You'll lead projects spanning multimodal patient data analysis and knowledge-driven hypothesis generation, working at the intersection of machine learning, computational biology, and pharmaceutical innovation.

Responsibilities

• Lead the development and application of agentic AI systems for drug and target discovery, spanning data-first and knowledge-first discovery paradigms
• Direct projects discovering novel drug targets, biomarkers, and disease subtypes through reverse translation using multi-omics patient data (genomics, transcriptomics including spatial and single-cell, proteomics)
• Build and manage a high-performing team of data scientists, research scientists, and AI scientists
• Collaborate closely with biomedical experts including biologists, physicians, and computational chemists on strategic discovery initiatives
• Partner with agentic engineers, ML engineers, and software engineers to translate research concepts into functional scientific tools
• Provide scientific input to anticipate pharmaceutical partner needs and shape platform capabilities
• Communicate scientific results to internal stakeholders and external collaborators
• Contribute to methodology development through literature review and scientific innovation

What We're Looking For

Required Qualifications:

• PhD in Bioinformatics, Computational Biology, Data Science, Computer Science, or Machine Learning with strong computational biology knowledge (or equivalent high-level industry experience)
• Deep expertise working with multimodal omics datasets including bulk RNA-seq, single-cell, spatial transcriptomics, WGS, WES, GWAS, and proteomics
• Expert-level Python programming skills with focus on building production-quality, modular code that interfaces with complex APIs and databases
• Publication record in top-tier scientific journals and conferences
• Fluent English communication skills with ability to explain complex technical concepts to diverse audiences
• Proven stakeholder management abilities across cross-functional teams
• Collaborative, team-first mindset with experience leading scientific teams

Preferred Qualifications:

• Expertise in agentic AI systems including LLM reasoning (Chain-of-Thought, ReAct), long-context management, and tool-augmented generation
• Experience with additional medical data modalities relevant to drug discovery (histopathology imaging, electronic health records)
• Systems engineering experience training and deploying large-scale models and managing multi-agent orchestrations
• Causal inference expertise, particularly understanding treatment response and causal discovery in biological systems
• Track record of translating research concepts into functional tools used by other scientists
• French language skills are advantageous

How We Work With You

Candidates apply directly through Get A Job.ai. Our recruiting team will screen your application and conduct an initial interview to understand your background and goals. Qualified candidates are then presented to our client for consideration. We coordinate all communication and interview scheduling throughout the process. Please do not attempt to contact the client organization directly, as this may disqualify your application.

Pay

Compensation details will be discussed during the screening process based on experience and qualifications.

Equal Opportunity: Get A Job.ai is committed to equal employment opportunity. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other protected characteristic.