About Glyphic Biotechnologies
At Glyphic Biotechnologies, we are developing a massively parallel, single-molecule proteome sequencing platform that will transform life science discovery and usher in a new era of insights into human biology and disease. We have raised >$80M from venture partners and non-dilutive grant funding to achieve our vision of next generation proteome sequencing.
About the Role
We are looking for a Director-level technical leader to build and lead Glyphic’s Data Science function. This is a “player-coach” role, requiring you to be technically deep enough to guide signal-processing and ML strategy for a novel nanopore-based protein sequencing platform, while also building the team culture, processes, and infrastructure needed to scale a larger data organization. You will report to the VP of R&D and work closely with team leads in assay development, chemistry, and automation. This is a hybrid role with expectations to spend as much as ~20% of your time on-site with the team in Berkeley, CA (on average) in service of a more complete understanding of Glyphic’s technology and calibration with the on-site research team. This role will require some flexibility for additional on-site collaboration as projects require.
What You'll Do
Technical Leadership
- Set the technical direction for ML model development: amino acid classification from nanopore current signals, signal segmentation, stall detection, temporal modeling, and multi-cycle analysis.
- Drive improvements to classification accuracy through better architectures (transformers, deep learning), training strategies, and feature engineering.
- Own the roadmap for data infrastructure: pipeline automation, data lake architecture, metadata standards, and self-serve analytics for the broader scientific team.
- Make strategic build-vs-buy decisions for tooling, compute, and third-party platforms.
People Management
- Provide technical and professional management to a team of data scientists and engineers to enable end-to-end analysis pipeline.
- Create an environment where high-autonomy individual contributors thrive: clear goals, minimal process overhead, rapid feedback loops.
- Foster a culture of rigorous, reproducible analysis and clear communication of results to non-computational audiences.
Cross-Functional Partnership
- Translate wet-lab experimental goals into computational strategies and vice versa — surface data-driven insights that reshape assay design and instrument operation.
- Work with assay development to design experiments that generate high-quality training data and enable systematic evaluation of new chemistries (expanders, linkers, barcodes).
- Collaborate with the Head of Automation and hardware teams on instrument data integration and real-time analysis capabilities.
- Represent Data Science in management discussions, communicating progress, risks, and resource needs clearly.
AI Strategy
- Champion the adoption of AI coding and analysis tools (Claude, Claude Code, etc.) across the data team and the broader organization.
- Evaluate how generative AI and LLMs can accelerate internal workflows: automated reporting, data exploration, code generation, and literature review.
What You Need
Required
- MS or PhD in a quantitative field (Computer Science, Electrical Engineering, Computational Biology, Bioinformatics, Statistics, or related).
- 10+ years of post-academic experience in the omics space (genomics, proteomics, or related fields).
- 4+ years of experience managing technical teams (data scientists, ML engineers, or bioinformaticians), including hiring responsibility.
- Ability and willingness to operate as a player-coach: setting strategy while remaining hands-on with data, code, and models.
- Exceptional ability to identify, hire, and develop talent while establishing and enforcing standards of excellence in data science.
- Capacity to develop both individual contributors and future managers within the team.
- Deep expertise in one of the following:
- Primary sequencing data analysis
- Machine learning applied to biological data
- Pipeline infrastructure and bioinformatics tooling
- Solid understanding of signal processing, classification, and machine learning techniques (transformers, CNNs, RNNs) and comfort applying them to sequencing or time-series data.
- Practical familiarity with AWS, Nextflow, and modern bioinformatics tooling.
- Demonstrated ability to work at the bench-to-computation interface in collaborative research environments.
- Ability to present complex technical results to non-technical stakeholders and to translate biological questions into computational approaches.
Nice to have
- Direct experience with sequencing data, basecalling, read-level QC or nanopore signal-level analysis (strongly preferred).
- Experience building data infrastructure and analytics platforms in early-stage biotech.
What you can expect from this role
Work environment
- Collaborative culture where your ideas and expertise are valued.
- Direct impact on product development and company direction.
Professional growth
- Build Glyphic's first dedicated data science management function, defining team structure, standards, and culture.
- Help define the technical standards and best practices for omics data analysis while mentoring the next generation of data scientists who will adopt and advance these approaches.
- Work with proprietary, information-rich data at scale that few organizations possess—the opportunity to develop novel approaches and methodologies that set benchmarks for the field.