(Associate) Scientist, Health Data

The Health Data team at Variant Bio turns data generated in the field, across our international partner cohorts, into harmonized, analysis-ready resources that power genetic target discovery. We are looking for someone who enjoys the challenge of complex, real-world data and is motivated to build robust, reproducible solutions for maximizing its quality and comparability across studies, modalities, collaborators, and collection sites. In this role you will own the curation, quality control, and harmonization of multi-omic, survey, biochemistry, and clinical data as it arrives from field sites around the world. You will help develop the standards and pipelines that keep that data trustworthy at scale, spanning the full path from bench work to downstream analysis. Success in this role depends on good scientific judgment about how data is produced: an instinct for where recruitment, sampling, batching, or measurement can introduce bias. We value curiosity and resourcefulness alongside technical skill. There is room to grow from running and adapting existing processes toward owning pipelines and contributing to downstream analyses.

• →Curate, clean, and harmonize multi-omic, survey, biochemistry, and clinical data from international field sites and partner cohorts, reconciling differences across studies, platforms, and sites. 
• →Build reproducible QC (validation checks and anomaly detection) that flags problematic data; drive solutions to trace root causes and suggests long term solutions.
• →Contribute to study design, sample selection, and randomization schemes for multi-omic data generation.  
• →Catch data issues early and work with the internal team overseeing data collection to resolve them at the source. 
• →Run and adapt the ETL/ELT pipelines that ingest field data with room to own them over time. 
• →Prepare analysis-ready datasets, analyses, and quality assessments that feed downstream target discovery, with clear documentation throughout. 
• →Support quality data generation through occasional wet lab work including aliquoting, protocol testing, and sample QC.

• MS or PhD in epidemiology, biostatistics, genetics, bioinformatics, data science, public health, computational biology, or a related field - or bachelors with a minimum of 2 years relevant professional experience. 
• Demonstrated experience with large, imperfect real-world datasets (multi-omic, survey, biochemistry, and/or clinical) and a track record of catching quality issues before they reach an analysis and tracing them back to their cause.
• Basic proficiency in R, Python, or a similar programming/scripting language. 
• Genomic study-design literacy: Able to recognize when recruitment, sampling, batching, or measurement decisions could introduce bias or confounding, and what the resulting data can and cannot support.
• Prior wet lab experience in molecular biology or related context. 
• A learning mindset and creative problem-solving; comfortable working at the edge of your expertise and drawing on a cross-functional team to solve unfamiliar problems.
• Strong attention to detail and record-keeping, and the ability to manage parallel workflows across multiple studies. 
• Clear communication and comfort collaborating across a cross-functional, international team. 
• Ability to be onsite in the Seattle office a minimum of three days a week. 

• Experience developing and owning data pipelines; familiarity with workflow managers (e.g., Nextflow), databases (e.g. SQL), version control (Git), and HPC/cloud environments (e.g., AWS, Slurm).
• Experience with REDCap or similar electronic data capture (EDC) platforms. 
• Familiarity with data harmonization frameworks — common data elements, common data models, or FAIR principles, including metadata schemas and data dictionaries.
• Familiarity with clinical data including biochemistry, anthropometrics, and health history. 
• Experience with multi-omic data (proteomic, transcriptomic, metabolomic) generation, and an understanding of how multi-omic and phenotypic evidence is used to identify and prioritize therapeutic targets. 
• Prior experience with nucleic acid extraction and QC, qPCR, and/or ELISA assays. 
• Experience with human subjects research, IRB/ethics workflows, or international multi-site collaborations.
• Familiarity with biomedical coding systems and ontologies (ICD-9/10, Phecodes, HPO, etc.) for defining and harmonizing phenotypes across cohorts.