Director, Data Science

Glyphic Biotechnologies•Berkeley, CA

4h•$215,000 - $257,000•Hybrid

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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