Job Description
Who we are:
This is a company that is fueled by connections. We thrive in a supportive, fun, and flexible environment full of people empowered to bring their whole selves to work. We care deeply about our work, each other, and the patients who count on us. Our teams cultivate strong bonds with patient communities, healthcare professionals, partners, and colleagues, which helps us discover, develop, and deliver therapies for genetically defined diseases – and make a bigger difference in their lives. In the U.S., this company markets a first-in-class pyruvate kinase (PK) activator for the treatment of hemolytic anemia in adults with PK deficiency. Building on the company’s leadership in the field of cellular metabolism, we are advancing a robust clinical pipeline of investigational medicines with active and planned programs in alpha- and beta-thalassemia, sickle cell disease, pediatric PK deficiency and MDS-associated anemia.
The impact you will make: This company is searching for a dynamic Sr. Scientist, statistical genetics to join our Data Science team. This role involves leading strategies for population-scale genetics and genomic datasets, including Real-World Evidence data like UK Biobank. The candidate will identify actionable intervention points, such as indication expansion, and serve as a key partner in shaping genetic strategies across the company's drug portfolio. Responsibilities involve developing analytic strategies for rare diseases, utilizing internal and public data to identify disease mechanisms, and providing support for all phases of research and development using high-dimensional Next Generation Sequencing data. Cross-functional collaboration is essential to interpret data and provide practical insights.
What you will do: · Be a key contributor of creating target discovery and precision medicine strategies across therapeutic areas
· Design and implement discovery pipelines based on human genetics data to understand disease mechanisms, identify patient subpopulations, provide insights into potential side effects, and discover new targets.
· Design and implement genetic statistical methods for GWAS/RVAS from biobank-scale data and meta-analysis summary statistics, including fine mapping, colocalization, functional enrichment, Mendelian randomization, and LDSC.
· Integrate multiple omics data to support and advance the understanding of drug mechanisms of action.
· Tackle complex problems, anticipate and overcome challenges, and forecast timelines for deliverables to support program objectives.
· Collaborate with computational scientists, biologists and disease experts to validate disease mechanisms and patient subpopulations.
· Communicate results to the project team, the company scientific community through internal documents, presentations, and publications.
· Establish best practices among Data Sciences analysts and help the Data Sciences infrastructure development.
· The candidate is expected to keep a keen eye on emerging methods in statistical genetics.
What you bring: · Ph.D. in statistical genetics, Bioinformatics/Computational Biology with at least 2 years of experience.
· Proven experience in analyzing high-dimensional genomics with associated phenotype
· Strong foundational knowledge about population genetics (GWAS, QTL mapping, heritability estimation, etc.).
· Experienced in the analysis of human genetic DNA sequencing data (variant annotation & classification, patient stratification, etc.).
· Demonstrated ability to interrogate and analyze genotype-phenotype associations (GWAS & PheWAS), mutation/variance burden testing, and the ability to translate the analysis results into new biological insights and program decisions.
· Proficient with processing high-dimensional Real world evidence.
· Hands-on programing skills are essential: Python, and/or R/Bioconductor, Unix & cluster computing programming and pipeline development.
· Experienced with data visualization tools and creating reproducible workflows with an emphasis on Unix, Git, and RMarkdown and Jupyter.
· Strong communication skills demonstrated with peer-reviewed publications and/or conference presentations.
· Excellent organizational skills and demonstrated ability to influence and drive decision-making.
· Deep curiosity about emerging trends in statistical genetics, machine learning and AI.
· Prior experience with large-scale human genetics and genomic epidemiology studies, such as UK Biobank/ All of Us Research Program / TOPMed and similar population level cohorts is strongly preferred
· Coding experience in Python and other statistical genetic software.
· Ability to analyze multiple Omics datasets (DNAseq, RNAseq, proteomic, etc.) is a plus
· Experience and/or training in rare genetic diseases is preferred.
Job Tags
Remote job, Contract work, Flexible hours,