Head of Computational Biology

Alta Resource Technologies, Inc.•Boulder, CO

3d•$155,000 - $215,000

About The Position

The Head of Computational Biology will lead hands-on technical execution and build and manage a small team. The role integrates Alta’s design-build-test-learn data with in silico and bioinformatics approaches to improve predictions of protein expression, activity, specificity, and manufacturability. A central focus is translating computational models into experimentally validated candidates that perform in the lab, can be manufactured at scale, and advance toward commercialization. The role develops computational approaches connecting protein design, experimental results, and predictive modeling to accelerate iterative learning. It works closely with the Applied Biology group, LIMS and software developers, and Analytical Chemistry groups to define data requirements, experimental workflows, and automation systems that support data quality, provenance, and reliability. The position translates research needs into scalable workflows that integrate sample tracking, data capture, analysis, and model feedback. The ideal candidate combines deep computational expertise and scientific rigor with cross-functional communication and the ability to move between analytical problem-solving, computational development, and team leadership.

Requirements

Ph.D. in computational biology, bioinformatics, or a related field.
5–10 years of proven success in cross-functional leadership.
Demonstrated people leadership, including directly managing, hiring, and developing a team of scientists while remaining a hands-on individual contributor.
Track record of delivering outcomes by building design/test systems that produced commercially relevant protein, peptide, antibody, or similar candidates advanced toward commercialization.
Expertise in machine learning and predictive models focused on protein engineering workflows.
Expertise in bioinformatics pipelines and databases.
Experience with generative AI models.
Experience with molecular dynamics and quantum mechanics parameterization.
Experience with LIMS and sample management systems.
Knowledge of experimental design, the scientific method, and statistical analysis.
Ability to build well-documented and version-controlled data pipelines.
Strong written and verbal communication skills and ability to work across different teams.
Strong organizational and documentation skills.

Nice To Haves

Experience working within or alongside build and test functions, not only design and learn.
Experience integrating computational workflows with laboratory automation and high-throughput build/test platforms.
Experience with multiomics data pipelines, flux balance analysis, and metabolic pathway modeling.
Experience with antibody engineering simulation pipelines.
Experience with Git, AWS, and standard computational tools.

Responsibilities

Lead a group developing model classifications and scoring for protein candidates and chemistries using in silico and real-world data sources.
Establish protein and peptide molecular dynamics and quantum mechanics workflows for in silico scoring.
Execute workflows hands-on, contributing to model development, code, and analysis alongside the team.
Define success metrics tied to company outcomes, including experimentally confirmed hits, manufacturable leads, and candidates advanced toward commercialization.
Build pipelines from existing sequence databases and 3D modeling data.
Work with a team to develop and implement model fine-tuning using high-throughput performance data.
Use protein manufacturing data to enhance workflows and predict lead candidates for scalable production.
Deploy generative AI approaches to streamline workflows.
Collaborate with the data science team to implement genotype-to-phenotype relationship tracking for model refinement.
Work across teams to build a tracking system for proteins across design, build, test, and scale workflows.
Use sequencing data, production data, and performance tracking to build a system for multiscale protein and genome engineering.
Partner with build and test teams to integrate computational workflows and account for real-world constraints such as throughput, cost, and turnaround time.
Hire, directly manage, mentor, and develop a small team of computational scientists; set clear goals and hold the team accountable for delivery.
Balance hands-on individual contribution with team leadership as a player/coach.
Ensure excellent documentation practices.
Communicate results effectively with stakeholders across the company.
Develop talent through team building and project execution, and foster collaboration between computational, build, and test teams.