Principal Scientist, Translational AI Foundations

Position Summary

Translational IPS is forming Translational AI Foundations (TAIF), a small team of computational biologists and AI engineers that works side by side with our translational research groups. TAIF turns the biomarker analyses scientists already perform by hand into validated, reusable AI agents and workflows, then transfers each one to the research group that will use and maintain it.

We are seeking a Principal Scientist to design, build and validate the team's assay-level AI agents and the analysis logic behind them, so that a biomarker analysis that takes weeks of hands-on work today can be run, reviewed and carried to a recommendation in days. The initial focus is spatial transcriptomics and H&E, followed by other data types (ctDNA, IHC, cytokine panels, RNA-seq, PK/PD and clinical data).

Key Responsibilities

• Build and maintain AI agents for the major biomarker data types (spatial and H&E first, then ctDNA, IHC, cytokine, RNA-seq, PK/PD and clinical data), each with defined inputs and outputs, a traceable source for every number and a human-review step.
• Write each agent's specification with the organization's subject-matter experts: the biology being measured, data and quality control, the approved analysis flow, acceptable and unacceptable interpretations, known failure modes, gold-standard reference cases and the expert reviewer.
• Set the test cases and pass criteria every agent must meet before release, and run periodic peer review of agent outputs.
• Define and validate the biomarker definitions, cutoffs and derivations for each assay, which the platform engineering scientist encodes in the team's shared ontology.
• Own the output template: every result ships with an interpretation, its limitations and a recommendation, in a form suited to program decisions.
• Lead the initial pilots in spatial transcriptomics and H&E and measure the time saved against current practice; then extend coverage to further data types and research groups in priority order.
• Work with the AI contacts in translational research groups: co-develop their first tools, review what they build for scientific correctness, and teach the methods.
• Maintain each agent's validation tests after hand-off to the research group that owns it, and confirm that it still passes them.
• Document methods and specifications to a standard others can build on, mentor junior team members and the AI contacts you work with, and help raise the computational and AI maturity of the broader translational organization.

Basic Qualifications

Ph.D. in computational biology, bioinformatics, biostatistics, computer science or a related quantitative field, or in a life-science field with substantial computational training, with 4+ years of relevant academic and/or industry experience or Master's degree with 6+ years or Bachelor's degree with 8+ years.

Preferred Qualifications

• Demonstrated hands-on experience analyzing and interpreting high-dimensional molecular data from human tissue, including spatial transcriptomics (for example MERFISH, Xenium, Visium HD or CosMx) and single-cell or bulk RNA-seq, carried through to publication-ready or decision-ready results.
• Hands-on experience building LLM-based or agentic analysis workflows (tool-calling agents, multi-step LLM pipelines, retrieval-grounded annotation) and evaluating their outputs against expert review, reference results or benchmarks.
• Strong Python and/or R with reproducible analysis practice, and a record of turning recurring analyses into reusable tools that other scientists adopted.
• Clear scientific communication and experience working directly with wet-lab, translational or clinical colleagues who are not engineers.
• Hands-on analysis of patient-derived translational data in at least three of: IHC, cytokine panels, bulk or single-cell RNA-seq, ctDNA, PK/PD, spatial transcriptomics, H&E pathology; and a working understanding of pharmacodynamic, mechanism-of-action and mechanism-of-resistance questions in early clinical development.
• Spatial analysis depth: deep-learning cell segmentation, atlas-based automated cell-type annotation, spatial differential expression, neighborhood and ligand-receptor analysis; familiarity with the analysis stack (Squidpy, SpatialData, scverse) and with digital pathology tooling (HALO, QuPath).
• Evaluation design for AI output: benchmark sets and gold-standard cases, reference results, consistency checks across models, checks for claims the data do not support, and experience writing the specification an expert reviewer signs off on.
• Reproducible engineering practice: version control, tests, workflow managers (for example Nextflow or Snakemake), cloud or HPC compute (for example AWS), CI/CD, and experience packaging analyses as command-line tools, dashboards, agent tools or services that others adopted.
• Experience training and enabling scientists who are not engineers: workshops, courses, documentation, co-design, and methods that others then used on their own work.
• Prior experience in biopharmaceutical translational research or early clinical development, including biomarker strategy or clinical translational data interpretation.
• Familiarity with governed data platforms (for example Databricks) and with controlled terminologies or ontologies for biomarker data.
• A record of methods or tools evidenced by peer-reviewed publications, released software, or deployed analysis portals in spatial or single-cell biology.