What We're Looking ForOur data infrastructure is growing faster than our ability to govern it. We need someone who thinks in systems, speaks the language of computational biology, and can build the standards, tooling, and team structure that lets our science scale without breaking. This role owns our in silico and data operations: how assay data flows from raw output to structured insight, how our bioinformatics team is organized and directed, and how we evaluate, implement, and scale platforms like Benchling. You are native in AI, not just tool-adjacent, but someone who reaches for it instinctively as part of how you build and think. This role is fully remote and open to candidates anywhere in the United States. You will report to an East Coast-based leader and work closely with our existing bioinformatics team. Over time, as trust is established, this role will take on direct management of data scientists on the team.
What You'll Own- LIMS ownership: our current LIMS implementation is struggling, and you will own the path forward - either fixing it or evaluating and leading a replacement (Benchling, Dotmatics, or comparable). This role requires a full-ownership mentality, not a recommendation-from-the-sidelines posture
- Data infrastructure and standards: repeatable formats for raw assay data, ASO design files, in vitro and in vivo readouts, and CRO deliverables; you direct the bioinformatics team to execute against those standards
- Decision and program tracking systems: a single source of truth for what is being done, by whom, by when, and what comes next - from a data and decision infrastructure perspective
- AI-native workflows: build and maintain AI-assisted pipelines for data analysis, literature synthesis, and scientific decision support
- Bioinformatics team direction: work closely with bioinformaticians (and over time manage data scientists) to ensure computational outputs meet scientific and operational standards
- CRO data quality: own the incoming data QC process for computational and omics deliverables; escalate and remediate when vendor outputs fail standards
- Close partnership with the Wet Lab Scientific Operations Director to keep experimental and computational programs in sync, including shared raw-data standards adopted across CROs and internal workflows
Goals for the First 90 Days- Audit the current data infrastructure across SYT1 ASO 1, ASO 2 screen, the cocktail program, and siRNA exploratory work; produce a clear, written assessment of gaps and recommendations
- Define the raw-data standard for assay outputs (knockdown, off-target, tox, PK readouts) and have it adopted across at least two CROs and internal workflows
- Deliver a written recommendation on the LIMS - fix in place or replace with Benchling (or comparable) - with implementation plan and timeline
- Stand up a program tracker and decision log that the founder, lead scientist, and senior advisors actually use
- Establish a working relationship with the bioinformatics team and identify any gaps in tooling, documentation, or workflow standards
Minimum Qualifications- PhD in computational biology, bioinformatics, biostatistics, computer science, or a closely related field
- 7+ years in computational biology, bioinformatics, or data science within a drug discovery or biotech context
- Hands-on experience building and owning data infrastructure in a small, resource-constrained organization - not just contributing to it
- Comfort with nucleic acid sequence design and navigating sequence design space (ASO, siRNA, or related modalities)
- Fluency with cloud data platforms (GCP, AWS) and bioinformatics workflow tools
- Native AI user - you build with AI, not around it; tools like Claude, Copilot, or equivalent are a daily part of how you work
- Experience evaluating or deploying scientific data management platforms (Benchling, Dotmatics, or comparable)
- Track record of building operational systems and standards from scratch
- Comfort directing senior PhD scientists without being one - credibility comes from clarity and precision, not titles
Preferred Qualifications- Prior experience integrating wet lab and dry lab experiments into a coherent pipeline - from assay design through data structure to interpretation
- Experience with oligonucleotide, siRNA, or gene therapy program data - familiarity with the data types and QC challenges specific to nucleic acid therapeutics
- Prior management or formal mentorship of data scientists or bioinformaticians
- Familiarity with regulatory data requirements in a preclinical-to-IND context
- Experience inside a small biotech, founder-led nonprofit, or intensely mission-driven organization
- Working knowledge of bDNA / QuantiGene, ddPCR, or qPCR data artifacts in nucleic acid quantification
Behavioral Essentials- Execute independently from loosely specified tasks
- Ask for help only when truly blocked, communicating clearly what's needed
- Thrive in early-stage, ambiguous, high-pace environments
- Mission-driven with genuine care for patient impact
