[Hiring] Computational Biologist @Pty

Role Description

We are partnering with an innovative biotechnology company that leverages advanced computational biology and data science to accelerate biotherapeutic discovery. In this role, you will bridge the gap between computational approaches and biological science, developing structured datasets and analytical frameworks to support modern protein engineering workflows. If you are passionate about applying data-driven methodologies to advance antibody and therapeutic protein development, this position offers an exciting opportunity to drive impactful biological research.

Duties & Responsibilities

• Dataset Curation & Benchmarking: Construct and curate standardized biological datasets from real-world antibody and protein engineering programs to evaluate computational pipeline performance.
• Algorithm & Model Testing: Establish quantitative evaluation frameworks and testing protocols to measure the accuracy, reproducibility, and utility of advanced computational biology tools.
• Data-Driven Prioritization: Perform variant ranking, enrichment analysis, and sequence-to-function mapping to set rigorous performance standards for therapeutic candidate selection.
• Cross-Functional Collaboration: Partner with software engineers, computational scientists, and wet-lab teams to translate complex biological workflows into scalable data structures and computational models.
• Therapeutic Analytics: Analyze binding kinetics, biophysical stability profiles, and cell-based assay data to validate computational predictions and optimize lead molecules.

Qualifications

• 2+ years of hands-on experience in biologics discovery or development (antibody engineering, protein engineering, or target validation).
• Proven experience interpreting binding kinetics (SPR/BLI), biophysical stability data, and cell-based potency assays.
• Strong proficiency in Python and/or R for data analysis, pipeline integration, and scientific visualization.
• Familiarity with at least one of:
  • Antibody sequence optimization (CDR engineering, VH/VL pairing, variant library design).
  • Structural biology and computational modeling tools (e.g., AlphaFold, molecular docking, molecular dynamics).
  • High-throughput binding screens and automated assay analysis.
  • PK/PD modeling for biotherapeutics (target occupancy, clearance, half-life considerations).
  • Bioprocess, expression, or manufacturability constraints.

Nice-to-Have

• Familiarity with immunogenicity prediction and risk assessment toolkits.
• Experience designing quantitative potency assays (ELISA, biochemical, or cell-based systems).

Package & Remuneration

TBD

Interested?