Director - Cheminformatics

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 #J-18808-Ljbffr The University of Texas MD Anderson Cancer Center