Computational Biologist, Immune Cell Repolarization

The Team

Our immune cell reprogramming team integrates foundational research on immunology and disease biology with AI-modeling to develop engineered cells that harness our own immune system to detect and treat early signs of age-related diseases, like cancer, Alzheimer's, and Parkinson's. These technologies will enable precise, context-dependent therapeutic responses only when and where it is needed. You can learn more about our work here.

Our work brings together three powerhouse universities - Columbia University, The Rockefeller University, and Yale University - into a single collaborative technology and discovery engine.

Our Vision

• Pursue large scientific challenges that cannot be pursued in conventional environments
• Enable individual investigators to pursue their riskiest and most innovative ideas
• Facilitate research by scientists and clinicians at our home institutions and beyond

We are a team of passionate individuals powered by technology, guided by scientific research, and driven by collaboration, working toward a mission to cure or prevent all disease.

The Opportunity

Biohub NY is seeking an accomplished computational biologist experienced in machine learning and transcriptomic data analyses to join our interdisciplinary team.

Within the Biohub NY "Immune Cell Re-Programming" group, this role requires experience in research settings, a background in biology, and a proven ability to design, evaluate, and publish innovative computational methodologies that leverage machine learning, statistics, and multi-omics to advance biological research and discovery.

Research projects to accelerate the rate of scientific discovery will be assigned by the group leader, Dr. Aleksandar Obradovic, and in collaboration with research teams across the organization.

Dr. Obradovic's group focuses on leveraging novel approaches for analysis of transcriptional, TCR-Seq, and spatial data across clinical and pre-clinical data toward improved understanding of the immune mechanisms of resistance to checkpoint-inhibitor immunotherapies, with projects aimed at inferring and prioritizing synergistic combination-therapies and regulatory targets for re-programming immune micro-environment (T-cells, macrophages, fibroblasts) toward an anti-tumor phenotype.

The ideal candidate will have a strong track record of accomplishments and a dedication to collaborative work within a highly interdisciplinary environment. Please submit a cover letter with your resume.

What You'll Do

• Contribute to a dynamic, innovative, and collaborative program that aligns with the mission of Biohub NY.
• Develop, apply, and evaluate cutting-edge computational / AI methodologies using data generated from across all research groups and incorporating relevant available datasets to develop mechanistic models of tumor-immune-stromal crosstalk.
• Collaborate within an interdisciplinary research environment to develop, test, and validate models.
• Engage with colleagues throughout the Biohub to uphold our values of scholarly excellence, innovation, open communication, hands-on hacking, and partnership.
• Communicate progress and results with colleagues inside and outside of your team.
• Publish and disseminate impactful findings through preprints (medRxiv, bioRxiv) and/or software repositories (e.g., GitHub).
• Work with the Biohub team to patent and license technologies resulting from your research.

What You'll Bring

• PhD in Systems Biology, AI / Machine learning, Statistics or MS plus relevant job experience.
• 1-2 years of relevant biomedical science experience, demonstrating a deep understanding of cellular biology, transcription and protein signal transduction.
• Experience demonstrating the ability to implement, evaluate, and create new computational methodologies that leverage machine learning, statistics, and AI for biological research and discovery.
• Experience programming in R and Python.
• Experience in building and evaluating machine learning and/or neural network models on biological data, with a deep understanding of feature selection, regularization, model introspection, and interpretability.
• Proficiency in using and modifying probabilistic learning or deep learning models such as RNNs, GNNs, protein sequence models, or natural language processing models.
• Proven track record of individual innovation, as well as a strong ability to work collaboratively.
• Outstanding interpersonal and communication skills.
• Demonstrated commitment to open science and alignment with the mission and values of Biohub.

Compensation

The New York City, NY base pay range for a new hire in this role is $153,000 - $191,000. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process.

This position may be eligible to participate in our discretionary annual performance bonus program. Bonus eligibility and targets are determined in accordance with our total rewards philosophy and may vary by role.

Better Together

As we grow, we're excited to strengthen in-person connections and cultivate a collaborative, team-oriented environment. This role is a hybrid position requiring you to be onsite for at least 60% of the working month, approximately 3 days a week, with specific in-office days determined by the team's manager. The exact schedule will be at the hiring manager's discretion and communicated during the interview process.

Benefits for the Whole You

We're thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible.

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice.
• Funding for select family-forming benefits.
• Relocation support for employees who need assistance moving

• If you're interested in a role but your previous experience doesn't perfectly align with each qualification in the job description, we still encourage you to apply as you may be the perfect fit for this or another role.
• #LI-Hybrid #LI-Onsite