Senior Scientist, Computational Biology & Data Infrastructure

Responsibilities

• Own genomic analyses and biobank strategy: Run computational genomic safety analyses and author the supporting reports that underpin our regulatory and customer-facing safety documentation
• Maintain the whole-genome sequencing pipeline: assembly of incoming sequencing data, linkage of genomes to strain records, and curation of best-available assemblies for downstream use
• Own and operate the strain dereplication pipeline, advancing isolated colonies from primary 16S/ITS screening through unique-strain identification and entry into the biobank
• Design and curate screening plates for downstream functional assays, including layout, strain selection, and verification of taxonomy and safety annotations
• Build and own Kingdom’s AI-compatible scientific data infrastructure: Build AI-compatible data infrastructure for Kingdom that captures, stores, and analyzes data across a variety of modalities
• Develop and apply statistical and computational methods to power biological insights and support wet-lab workflows, including experimental design, metric development and validation, and method onboarding, powered with new AI/agentic tools
• Maintain the integrity of the strain database backend: tube tracking, naming conventions, and audits of our physical inventory and pipeline data to identify anomalies
• Model complex, interrelated scientific entities (samples, experiments, runs, results, physical inventory) into a coherent schema and ontology the team can work in
• Scope, build, and ship the data processing, analysis, and metrics that give new assays clear, validated benchmarks

Qualifications

• PhD (or equivalent depth) in computational biology, bioinformatics, microbial genomics, or a related quantitative life-sciences field; ~1-3 years post-PhD
• Domain depth in microbial genomics: whole-genome sequencing, 16S/ITS amplicon analysis, dereplication, taxonomy, and genomic safety annotation
• Strong knowledge of statistics and quantitative methods
• Advanced coding literacy: expert-level Python or R, proficiency with SQL, fluency with containerization for reproducible pipelines, comfort building tools that interact with third-party APIs, and strong code-quality practices (version control, testing, code review)
• Experience designing and maintaining bio-data infrastructure: proficiency modeling complex interrelated entities and comfort establishing and communicating an ontology
• AI-native: daily Claude Code or equivalent use, prompt fluency, comfort rebuilding research cadence around agentic tools, with experience building tooling around your own research
• High agency: self-starter who identifies pain points, proposes a plan and solution, and drives their own learning
• Builder mentality: comfortable with building a first version of a tool fast, and iterating to improve
• Bias to actionable output: instinct to package findings into something concrete that unblocks decisions and moves the next step forward
• Clear and effective written and verbal communication