Director, Data Science

About Glyphic: At Glyphic Biotechnologies, we plan to create the protein revolution for which scientists and researchers have been waiting. 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. To date, we have raised >$80M from venture partners and non-dilutive grant funding to achieve our vision of next generation proteome sequencing. What we are looking for in you We are looking for a Director-level technical leader to build and lead Glyphic’s Data Science function. This is a “player-coach” role. You must 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 later scale from a larger data organization. You will report to the VP of RD and work closely with team leads in assay development, chemistry, and automation. This is a hybrid role and 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 de ... (truncated, view full listing at source)