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

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