About the role

Statistical Genetics Platform Engineer leveraging human genetic data to drive decision making at Lilly. Collaborating with statistical geneticists and computational biologists to innovate and implement scalable computational pipelines.

Responsibilities

Design and implement robust, scalable computational pipelines for statistical genetics analyses, including workflows for GWAS, polygenic risk scores, fine-mapping, colocalization and variant annotation
Develop and maintain platform tools and APIs that enable researchers to efficiently process genomic data at scale (biobanks, population cohorts, multi-omics datasets)
Build infrastructure for reproducible research, including containerization, workflow orchestration, and version control for analytical pipelines
Optimize computational performance of statistical genetics algorithms and implement distributed computing solutions for large-scale analyses
Collaborate with statistical geneticists and computational biologists to translate methodological innovations into production-ready software
Establish best practices for data access, quality control, validation, and documentation across genomic analysis pipelines
Maintain and improve existing codebases, ensuring code quality, testing coverage, and comprehensive documentation
Monitor platform performance, solve issues, and implement improvements based on user feedback and evolving research needs
Support the integration of AI-based tools and required MLOps infrastructure

Master’s in Computer Science, Statistical Genetics, Bioinformatics or related field and 6+ years post-Master’s experience (in industry or large-scale non-academic institutions, e.g. Broad, NIH), OR PhD in Computer Science, Statistical Genetics, Bioinformatics or related field and 3+ years post-PhD experience (in industry or large-scale non-academic institutions, e.g. Broad, NIH)
Strong programming skills in languages commonly used in genomics research (Python, R)
Demonstrable understanding of statistical genetics concepts including GWAS, heritability estimation, genetic correlation, rare variant analysis, and population structure
Experience using standard tools and formats for genetic data (VCF, BGEN, PLINK, BAM/CRAM) and genomic databases
Proficiency with workflow management systems (Nextflow, Cromwell/WDL) and containerization technologies
Experience with high-performance computing environments, cloud platforms (AWS, GCP, Azure), or distributed computing frameworks
Strong problem-solving abilities and attention to detail in handling complex biological datasets
Ability to prioritize and manage multiple competing priorities within a fast-paced environment

eligibility to participate in a company-sponsored 401(k)
pension
vacation benefits
eligibility for medical, dental, vision and prescription drug benefits
flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts)
life insurance and death benefits
certain time off and leave of absence benefits
well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)