Ginkgo BioWorks

Senior Director, Multiomics

Our mission is to make biology easier to engineer. Ginkgo is constructing, editing, and redesigning the living world in order to answer the globe's growing challenges in health, energy, food, materials, and more. Our bioengineers make use of an in-house automated foundry for designing and building new organisms.

Senior Director, Multiomics Ginkgo Datapoints

Level (Sr. Director) will be set to match the successful candidate's experience.

Location: Boston, MA (Seaport / HQ) Reporting to: General Manager, Ginkgo Datapoints Focus: Scientific Excellence, Commercial Strategy (P&L), Functional Genomics, Arrayed Perturbation Response Profiling & Multiomic Platform Expansion

The Opportunity

Ginkgo Datapoints defines the category of the Bio AI data provider. We work with top pharma, biotech, tech, and techbio companies, and we've built our reputation on high-quality, large-scale, reproducible data generated by automation rather than by hand. We're a fee-for-service partner our customers own their data, and our incentives are aligned with theirs.

As our Senior Director of Multiomics, you will own the vision, execution, and commercial success of our Multiomics business, including the functional genomics engine behind our Perturbation Response Profiling and Specialized HTS offerings. This is a genuinely intrapreneurial role: you operate at the frontier of business and science, report to the GM of Datapoints, and lead a multidisciplinary team of scientists, automation engineers, and bioinformaticians with GS&A support.

Your Mission

Your primary goal is to scale our arrayed multiomic perturbation platform into the best in the industry, and to own the full P&L for the vertical. The near-term growth thesis is clear:

• Find new applications for DRUG-seq safety first. Expand our high-throughput transcriptomic screening into new use cases, with safety and predictive toxicology chief among them.
• Turn perturbation data into AI fuel. Position our datasets as the training substrate for the next generation of AI/ML models, and shape data generation specifically to serve model training.
• Round out the readout menu. Extend beyond transcriptomics and imaging into proteomics, metabolomics, and additional NGS methods that complete a true multiomic offering.
• Build products the industry doesn't have yet. Add new offerings to the portfolio arrayed whole-genome CRISPR knockouts paired with rich multiomic readouts among them and the drug-discovery power that kind of capability unlocks.

The role is anchored in discovery-stage functional genomics, with real room to extend our multiomic work into translational and clinical biomarkers population-scale and direct-to-consumer rather than regulated diagnostics. If that's the work that excites you most, there's a home for it here.

Key Responsibilities

Commercial & P&L Ownership: Define the go-to-market strategy for Multiomics Datapoints. Identify market gaps, set pricing for our high-value / low-cost-per-datapoint model, and own revenue growth and operational efficiency across a customer base that spans large pharma, biotech, and government.

Technical Vision & Roadmap: Set the scientific direction across our arrayed perturbation platform and multiomic readouts DRUG-seq (high-throughput transcriptomics), high-content imaging and Cell Painting (morphological profiling), and CRISPR-edit verification across arrayed chemical, genetic (CRISPR KO / CRISPRi), and siRNA / ASO modalities. Drive the expansion agenda above: new DRUG-seq applications (especially safety), data purpose-built for AI/ML training, new readouts (proteomics, metabolomics, additional NGS), and new products such as arrayed whole-genome CRISPR knockouts.

Strategic Team Leadership: Lead and mentor a multidisciplinary team of roughly 1015. Because Datapoints runs a central operations matrix, day-to-day task assignment flows through shared ops structures; your job is to set direction, develop the team, and bridge the gap between industrialized wetlab automation and computational/AI interpretation. A practical understanding of wetlab methods assay development, cell culture, perturbation delivery is essential, so that you can lead the experimental team credibly even if your own background is computational.

External Presence & Thought Leadership: Serve as the face of Ginkgo's Multiomics business. Represent the company at major conferences (e.g., the Festival of Genomics), publish and present our science, and cultivate community around initiatives like the Virtual Cell Pharmacology Initiative (VCPI). Lend scientific credibility to sales and BD conversations, and open doors by connecting the commercial team with relevant contacts across your network.

Government & Grant Strategy: Partner with Government Affairs to shape and win large-scale programs (ARPA-H, BARDA, DARPA, NIH). Our ARPA-H CATALYST program building an in-silico ADME-Tox and dosage-prediction platform aimed at reducing reliance on animal models is exactly the kind of safety-focused flagship you'll help drive.

Build the Best-in-Industry Arrayed Multiomics Engine: Lead the continued scale-up of our automated arrayed perturbation-and-readout platform transcriptomics, imaging, edit verification, and new omic layers with integrated QC and AI analytics pipelines, to produce high-fidelity, AI-ready datasets faster and at greater scale than traditional labs.

The Profile

We need a functional genomics leader who is bored with the status quo of "traditional" discovery. You should have deep respect for the craft of science, but a relentless dissatisfaction with its current speed and cost. You possess the rare combination of deep scientific intuition and a builder's ambition for scale and you're as comfortable in a customer conversation as in a data review.

Ideally, you've lived on the buy side. You've been an active buyer of discovery, CRO, or data-generation services, and you know firsthand where they fall short. Now you want to move to the sell side to build the offering you always wished you could buy and give the industry something genuinely new.

Required Qualifications

• Deep Industry Expertise: 10+ years in high-growth biotech or large pharma with a proven track record in functional genomics and discovery research. (We weigh hands-on industry R&D leadership more heavily than a purely academic track record.)
• Buyer's Perspective: You have been an active buyer of discovery / CRO / data-generation services (or led teams that were), and you're motivated to build, on the sell side, something you wished existed as a customer.
• Scientific Credibility: PhD (or equivalent experience) in genomics, molecular biology, computational biology, systems biology, or a related field. You are a recognized expert in perturbation biology or modeling and the discovery cascade.
• Perturbation Depth: Direct experience with arrayed chemical and/or genetic screening (CRISPR KO / CRISPRi), transcriptomic profiling, and high-content imaging.
• Computational Strength with Wetlab Command: A strongly computational profile is very welcome even ideal. What's essential is a firm, practical understanding of wetlab methods, so you can lead the experimental team credibly.
• Commercial Interest & Drive: You're excited to own the commercial side of a business pricing, growth, and turning technical capability into customer value. Prior P&L experience is a plus, but the appetite matters more than the rsum line; we're happy to help you grow into it.
• The "Ginkgo Mindset": You are an entrepreneur at heart. You thrive in fast-paced, high-stakes environments and are genuinely excited by the vision of a best-in-industry arrayed multiomics platform and the new products it can offer arrayed whole-genome CRISPR knockouts among them.

Preferred Qualifications

• Translational and clinical biomarkers: experience or a real passion for biomarker work in population-scale or direct-to-consumer settings (rather than regulated diagnostics).
• Multiomic expansion expertise: proteomics, metabolomics, or additional NGS methods that would round out the offering.
• AI/ML or automation ideally both: strength on either axis is a real asset. On the AI/ML side, experience using perturbation data to train models (MOA classification, perturbation embeddings, virtual-cell / gene-expression prediction). On the automation side, familiarity with how industrialized lab automation generates data at scale. Neither is required on its own, but candidates who bring both