An emerging biotech company at the intersection of artificial intelligence, medicinal chemistry, and automation is seeking a Director of Scientific Informatics to build and lead the scientific data ecosystem powering next-generation drug discovery.
This is a highly visible, hands-on leadership opportunity for someone excited by the challenge of integrating chemistry, biology, laboratory automation, and machine learning into a unified scientific platform. The successful candidate will help shape how data moves from the bench to predictive models and ultimately influences discovery decisions.
Responsibilities
- Own and administer core scientific informatics platforms supporting chemistry and biology workflows
- Lead integrations across ELN/LIMS, registration systems, inventory platforms, laboratory automation, cloud infrastructure, and downstream analytics
- Partner closely with biology, chemistry, automation, and machine learning teams to ensure scientific data is structured, curated, and accessible
- Establish data standards, metadata conventions, and governance practices across chemistry and biology datasets
- Improve assay data capture, analysis, reporting, and transfer workflows while reducing manual processes
- Serve as the primary point of contact for informatics vendors and external partners
- Develop SOPs and best practices for scientific data management
- Mentor and help grow informatics and data engineering capabilities as the organization scales
Qualifications
- BS, MS, or PhD in Chemistry, Biology, Bioinformatics, Computer Science, or related discipline
- 10+ years of experience in scientific informatics, cheminformatics, or research informatics within biotech or pharmaceutical environments
- Hands-on experience with scientific platforms such as ELNs, LIMS, registration systems, assay data systems, or laboratory automation platforms
- Strong understanding of medicinal chemistry and biology workflows, including SAR analysis, DMTA cycles, and multiparameter optimization
- Experience building scientific data pipelines and implementing metadata harmonization and governance strategies
- Proven ability to work cross-functionally with Biology, Chemistry, Automation, Data Science, and Machine Learning teams
- Proficiency in Python and SQL
- Strong communication skills with the ability to balance strategic planning and hands-on execution
Preferred Experience
- Integrating predictive modeling and machine learning into discovery workflows
- Experience with cheminformatics toolkits such as RDKit or related technologies
- Familiarity with workflow and visualization platforms such as KNIME, Pipeline Pilot, or Spotfire
- Experience with AWS, Docker, REST APIs, and modern scientific data engineering practices
- Knowledge of active learning, Bayesian optimization, or ML-driven compound design
- Prior experience in startup or growth-stage biotech environments