Associate Director, Computational Biology

Title: Associate Director, Computational Biology

Company: Stealth Portfolio Company

Location: Boston, MA

Reports to: Chief Scientific Officer

About Our Company:

We are a stealth-mode biotechnology company developing next generation complex biologics, with an initial focus on high-impact targets in immunology. Our mission is to rapidly design and globally develop highly differentiated therapies for patients with life-threatening diseases.

Position Overview:

We are seeking a creative, motivated and solutions-oriented computational biologist to serve as the critical bridge between our wet lab and our computational team. This is a hands-on, individual-contributor role centered on data and systems architecture: designing how experimental and computational data are structured, integrated and made model-ready so that results from the lab flow efficiently into design and analysis, and insights flow back to the bench. Reporting to the CSO and working across the discovery research (wet-lab) and platform/engineering functions, the Associate Director will turn experimental data into analyses and model inputs, run computational structure-activity-relationship (SAR) analyses, and translate model outputs into clear, actionable guidance for bench scientists—operationalizing a tight “lab-in-a-loop” for our multi-specific biologics programs in autoimmune disease and immuno-oncology.

Key Responsibilities:

•       Own the data and systems architecture that connects the wet lab and the computational team—data models, schemas and integration patterns that make experimental results structured, queryable and model-ready.

•       Serve as the primary bridge between bench scientists and the computational and platform teams, translating experimental data into analyses and model inputs, and translating model outputs back into actionable experimental guidance.

•       Run computational SAR analyses to support iterative optimization of multi-specific biologics and to prioritize the most informative next experiments.

•       Integrate and interpret multi-omics, immunology and assay datasets to generate and test hypotheses and to inform Target Product Profiles.

•       Apply and deploy computational and machine-learning tools—structure prediction and generative design models, protein language models, and property predictors—and leverage agentic and LLM-based workflows to scale analysis and interpretation.

•       Partner with the platform and engineering team on requirements for pipelines, data infrastructure and model orchestration, and with the Director of Discovery Research on data-capture practices at the bench.

•       Communicate insights and recommendations clearly to scientific leadership and to bench colleagues, and contribute to a collaborative, results-driven culture.

Essential Qualifications:

•       PhD in computational biology, bioinformatics, systems biology, biological engineering or a related field with 6 years of relevant experience, including time in an industry/biotech setting; or an MS/BS with commensurate industry experience. Title and level will be set to match experience.

•       Demonstrated ability to design data models and architecture and to integrate heterogeneous experimental and computational datasets.

•       Strong programming skills (e.g., Python) and fluency working with large biological datasets.

•       Demonstrated ability to apply computational and ML methods to biological problems and to translate results into program decisions.

•       Excellent communication skills and a proven ability to work at the interface of experimental and computational teams.

Ideal Qualifications:

•       Experience supporting discovery of antibodies or other biologics, particularly multi-specific formats; familiarity with T-cell engagers (TCE) or other complex modalities is a plus.

•       Familiarity with contemporary protein modeling and design tools (e.g., AlphaFold/Boltz-style structure prediction, RFdiffusion/BoltzGen-style generative design, protein language models).

•       Experience with data infrastructure, ELN/LIMS integration, or building data pipelines in a discovery setting.

•       Experience leveraging agentic and LLM-based workflows in a research setting.

•       Domain knowledge in immunology, autoimmune disease and/or immuno-oncology.

Equal Opportunity:

Two River is an equal opportunity employer. We are committed to building a team with a range of backgrounds, experiences and perspectives, and we evaluate all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, status as a protected veteran, genetic information, or any other characteristic protected by applicable law. We provide reasonable accommodations to applicants and employees with disabilities.