Director, Data Science
About The Position
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. 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 R&D 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 collaboration as projects require.
Requirements
- 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 Haves
- 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.
- Navigates complex team dynamics, partnerships, and challenges with creativity and logic.
- Operates with adaptability, urgency, and flexibility in evolving environments, thriving in ambiguity.
- Drives work forward without needing to be asked, taking responsibility for outcomes rather than tasks.
- Treats obstacles as problems to be creatively solved, not reasons something can’t be done.
- Applies sound judgment to the best available information, testing, learning, and iterating.
- Shares early and directly when assumptions change, results are unclear, or timelines are at risk.
Responsibilities
- 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.
- 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.
- 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.
- 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.
Benefits
- Employee Stock Option Plan
- 100% Health Plan Coverage for Employees & Dependents (Medical, Dental, & Vision)
- Employer Retirement Contributions to 401(k)
- Generous Paid Time Off
- Paid Maternity and Paternity Leave
- Health & Wellbeing Program
- Office Snacks and Beverages
- Regular Team Bonding Activities
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What This Job Offers
Job Type
Full-time
Career Level
Director
Education Level
Ph.D. or professional degree
Number of Employees
1-10 employees
