Computational Biologist​/Data Scientist; Scientist or Senior Scientist

Code breaker is building a functional genomics platform to connect human genetic variation to cellular function ike standard CRISPR‑knockout Perturb‑seq workflows, Code breaker installs specific human variants via pooled HDR in primary human cells and reads them out with scRNA‑seq, giving variant‑level rather than gene‑level resolution. These datasets directly train Code breaker's proprietary variant‑effect scoring model, so the person in this role has unusually direct leverage over what the company can learn and predict.

This is a hands‑on scientific role. You will analyze large‑scale pooled‑variant Perturb‑seq and single‑cell functional genomics experiments, from primary data through perturbation‑effect estimation, differential expression, biological interpretation, and the generation of datasets for predictive modeling. At the senior level, you will lead this work. You will build scalable analysis pipelines while working directly with new datasets to understand the biology, diagnose problems and generate hypotheses.

You will also help shape the experiments we run. This requires a strong understanding of transcription and gene regulation, the capabilities and limitations of single‑cell RNA sequencing, and the statistical judgment to design experiments that can answer meaningful biological questions. You will work closely with experimental biologists to design variant installation experiments and disease‑ and cell‑type‑specific assays, and with computational and ML colleagues to use these data to understand and predict the consequences of genetic perturbations and human variation.

• Analyze Code breaker's variant Perturb‑seq and related single‑cell functional genomics experiments, from primary data through perturbation‑effect estimation, differential expression and biological interpretation.
• Build robust, scalable pipelines for QC, guide assignment, differential expression, perturbation‑effect estimation, visualization, and generation of model‑ready datasets.
• Apply statistical approaches for perturbational single‑cell data that account for experimental design, biological replication, perturbation efficiency, donor and batch effects, and cell‑state heterogeneity.
• Work directly with new datasets to separate biological signal from technical artifacts, find unexpected biology and generate hypotheses.
• Work with ML colleagues to build training and evaluation datasets and to test model predictions experimentally.
• Integrate single‑cell data with human genetics, regulatory annotations and sequence‑based predictions to interpret perturbation effects.
• Write reproducible, tested analysis code that holds up as experimental scale grows.

• Own the computational strategy for Code breaker's perturbation experiments and decide which analyses and experiments come next.
• Lead the design of Perturb‑seq experiments and disease‑ and cell‑type‑specific assays with experimental scientists, including perturbations, controls, replication, cellular context, sequencing depth and QC criteria.
• Develop new statistical methods where existing ones fall short.
• Set the scientific practices and infrastructure the computational team works within, and mentor junior scientists.

• PhD in computational biology, genomics, bioinformatics, systems biology, statistical genetics, biostatistics or a related quantitative field.
• Hands‑on experience analyzing single‑cell RNA‑seq data, and a working understanding of its biology, statistics and technical limitations.
• A solid understanding of transcription and gene regulation, and the ability to reason about how genetic perturbations produce molecular and cellular phenotypes.
• Sound statistical judgment in…