Job Description

Jnana Therapeutics, a wholly owned subsidiary of Otsuka Pharmaceutical, is a clinical-stage biotechnology company leveraging its next-generation RAPID chemoproteomics platform to discover medicines for highly validated, challenging-to-drug targets to treat diseases with high unmet needs. Jnana is focused on developing first- and best-in-class oral therapies to treat a wide range of diseases, including rare diseases and immune-mediated diseases. Jnanas lead program, JNT-517, which targets an allosteric site on the phenylalanine transporter SLC6A19, is a potential first-in-class oral approach for the treatment of PKU, a rare genetic metabolic disease. Located in Boston, Jnana brings together scientific leaders in small molecule drug discovery and development, and a highly experienced management team. For more information, please visit and follow us on Twitter/X and LinkedIn.

At Jnana, you'll join a diverse, passionate team dedicated to advancing therapies for challenging diseases. Our collaborative, purpose-driven culture fosters innovation, urgency, and belongingmaking Jnana a great place to work!

About the Opportunity

Jnana Therapeutics is seeking a proven Bioinformatics leader to establish and drive our bioinformatics capability. As a member of the Translational Sciences team, you will help set the vision and strategy for computational sciences at Jnana, while also making handson contributions to advance our drug discovery portfolio.

This is a unique opportunity to build from the ground updefining infrastructure, methodologies, and a high-performing team that will play a central role in shaping our science and pipeline.

Responsibilities

• Define and implement the bioinformatics strategy to support and accelerate Jnanas portfolio.
• Lead critical analyses and deliverables as an individual contributor, particularly in early stages of team buildout.
• Analyze and integrate transcriptomics, proteomics, metabolomics, and clinical/phenotype datasets to uncover mechanisms, identify biomarkers (predictive, PD, safety), and elucidate resistance/response signatures.
• Mine GWAS, rare-variant, and population datasets to validate targets, uncover risk genes, and generate mechanism hypotheses; translate genetic insights into biology and biomarkers.
• Architect scalable, versioncontrolled pipelines; build databases, repositories, and governance frameworks (QC, metadata, lineage, reproducibility); optimize cloud/onprem compute resources.
• Evaluate and implement machine learning approaches- including LLMs/NLP for literature and knowledge mining where they deliver measurable impact.
• Partner crossfunctionally across Jnana biology, chemistry, and translational science teams; mentor and grow a high-performing Bioinformatics group.
• Clearly communicate complex analyses and strategies to leadership and diverse stakeholders.

Desired Qualifications & Past Experience

• PhD (or equivalent) in Bioinformatics, Computational Biology, Human Genetics or related field.
• Minimum 7 years of experience in biotech or biopharmaceutical setting, including both people leadership and handson analytical contributions.
• Proven ability to design and scale bioinformatics infrastructure in a biopharma setting.
• Expertise in multi-omics integration (NGS, single-cell, proteomics, imaging, flow cytometry, clinical datasets).
• Strong background in human genetics (common/rare variation, statistical genetics, variation annotation) with application to target/biomarker decisions.
• Proficiency in programming languages (Python, R) and workflow/pipeline tools; experiences with databases, repositories, and data standards.
• Hands on experience with cloud platforms (AWS, GCP, Azure) for large scale data processing.
• Experience with AI/ML, NLP, LLMs and knowledge graphs is a plus. Demonstrate an AIfirst mindset and technical excellence with strong scientific background.
• Experience with scRNAseq and/or spatial omics is highly valued.
• Experience applying bioinformatics approaches in immunology is a plus.
• Ability to communication effectively, influence stakeholders, and foster innovation in a matrix environment.

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