Our client is a VC-backed startup developing in vivo genetic medicines to engineer the transcriptome and genome of diseased cells. They are combining protein and nucleic acid engineering, high-throughput screening, genomics, and computational biology to build a platform for rapid development and optimization of new therapies.
The Opportunity:
We are seeking a Head/Snr. Director of Computational Sciences to lead our client's full computational function: software/informatics infrastructure (LIMS), embedded research informatics, bioinformatics, machine learning and scientific computing. This role manages a team of approximately six scientists and engineers, including a Sr Fellow (Informatics), a Principal Scientist, 2 Staff Scientists, and 2 Senior Scientists.
The Head/Snr. Director’s highest-leverage contributions will be in driving execution across the computational stack, managing people development for the full team, and leading the infrastructure and bioinformatics functions. This person will be a technical leader across the organization—not only managing the computational team but driving improvements in experimental design and data practices across R&D, while building the software tools and infrastructure that make those improvements practical and sustainable. ML strategy is set collaboratively with the COO; the Head/Snr. Director translates that direction into team priorities while bringing their own technical depth to software systems, data architecture, and analytical rigor. This role has a clear growth trajectory toward VP/Head of Research Technology as the company scales.
Primary Responsibilities:
People Management & Strategic Contributions
- Manage a team spanning a wide range of seniority, disciplines, and working styles; foster a culture of commitment to craft, rigor, transparency, and collaboration
- Navigate interpersonal dynamics constructively, modeling productive conflict resolution
- Build productive partnerships across the R&D organization, combining technical leadership—driving adoption of principled experimental design, standardized workflows, and computational best practices—with strong product discipline around requirements gathering, user training, and iterative delivery of tools and pipelines that scientists actually use
- Provide leadership on the evaluation and implementation of AI productivity tools across R&D, including coding agents and chat-based assistants, ensuring adoption is practical, secure, and integrated into team workflows
- Contribute to R&D strategic planning and execution; support partnership milestone deliverables and due diligence preparation
Software & Informatics Infrastructure
- Provide hands-on technical leadership for our client's custom LIMS and supporting software infrastructure
- Own the LIMS and infrastructure roadmap execution, with clear milestones and a structured process for capturing and prioritizing feature requests
- Collaborate with the Senior Fellow (Informatics) and other members of the LIMS team on roadmap, technical architecture decisions, and software engineering best practices
- Oversee management of cloud infrastructure (AWS), balancing cost, performance, and reliability
Bioinformatics & Research Informatics
- Manage bioinformatics team members, ensuring pipelines meet the quality, rigor, and turnaround requirements of therapeutic programs and platform technology development projects
- Oversee guide/primer design, sequencing analysis, off-target analysis, flow cytometry analysis automation, and genomic characterization workflows
- Elevate bioinformatics from an operational support function to a strategic contributor—identifying opportunities where analytical capabilities can shape program decisions, improve candidate selection, and strengthen the data packages that support partnership and regulatory milestones
Machine Learning & Scientific Computing
- Partner with the COO on ML strategy—particularly around active learning and statistical experimental design—and translate that into team execution plans
- Oversee predictive model development, data pipelines, and computational tools that support therapeutic programs and platform technology development
Qualifications:
- 10+ years in computational biology, bioinformatics, or research software engineering in biotech/pharma, with 4+ years managing multi-disciplinary technical teams
- Strong software engineering background: application development, database design, API architecture, cloud infrastructure (AWS preferred)
- Working knowledge of ML concepts with the ability to manage ML scientists and translate strategic direction into execution; deep ML expertise is a plus but not required
- Experience with bioinformatics pipelines (amplicon, WGS, RNA-seq, or similar) and LIMS or research data platforms
- Working knowledge of AI productivity tools, including chat-based assistants and coding agents, with a perspective on how to deploy them effectively in a research software environment
- Strong communication skills with the ability to influence across technical and scientific audiences, from computational engineers to bench scientists to executive leadership
- Track record of managing and developing scientists and engineers across disciplines and seniority levels, including senior ICs
Education:
PhD in computational biology, bioinformatics, computer science, or related discipline with 10+ years industry experience; or MS with 14+ years. Background spanning software systems and quantitative biology strongly preferred.
Preferred Qualifications:
- Experience in gene therapy, gene editing, or genetic medicine development
- Direct experience developing custom LIMS or research software platforms
- Familiarity with molecular cloning and construct design workflows, NGS library preparation, and common sequencing approaches (amplicon, long-read, whole-genome)
- Familiarity with active learning, Bayesian optimization, or statistical experimental design in biological engineering
- Experience in a fast-paced, resource-constrained startup environment
