Job Title:Director – Cheminformatics

Job Number: 37527

Location: Remote,

Job Description

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
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