Senior Data Scientist

About UsAt DNAstack, our mission is to power precision medicine by building software that breaks down barriers to responsible biomedical data sharing, discovery, and analysis. We develop cutting‑edge technologies and industry standards to help researchers and clinicians analyze sequencing data and make faster, more accurate diagnoses. Our platform supports national and international networks tackling rare disease, cancer, infectious disease, and more. We’re a small but mighty startup team working at the intersection of bioinformatics, cloud computing, and open science.The RoleWe’re looking for an experienced Senior Genomics Data Scientist to join our nimble bioinformatics and AI team. This role is focused on the development and application of AI‑driven data science techniques to support bioinformatics and tertiary workflows across a federated network. You’ll be responsible for designing, optimizing, and maintaining AI‑powered analytical methods to extract insights from large‑scale genomic data.You’ll work both independently and directly with our research and clinical partners, applying your knowledge of genomics, machine learning, and data science best practices to develop scalable, intelligent models for genomic interpretation. You’ll help shape our AI strategy, integrate novel machine learning models into genomic workflows, and play a major role in advancing federated bioinformatics capabilities.What You’ll Be DoingDevelop and apply AI‑powered data science methods for analyzing large‑scale genomic datasets in a federated environmentDesign, train, and validate machine learning and deep learning models for genomics, including variant classification, functional annotation, and disease risk predictionCollaborate with partners in academia and industry to create models that process and harmonize diverse genomics datasetsApply large language models (LLMs) and generative AI for automated annotation, scientific literature mining, and variant impact predictionUtilize federated learning frameworks to train AI models on distributed genomic data while ensuring privacy complianceSupport the development of data harmonization and feature engineering pipelines to enhance AI model performance across genomic datasetsCommunicate complex AI‑driven genomic analyses clearly to technical and non‑technical stakeholdersStay current with emerging AI and deep learning techniques for genomics, participating in open‑source initiatives and advancing industry best practices.What We’re Looking ForMaster’s degree or PhD in data science, computational biology, bioinformatics, AI/ML, biostatistics, or a related field—or equivalent industry experience4+ years of hands‑on experience applying data science and machine learning techniques to genomics datasetsStrong experience implementing AI‑powered bioinformatics tools and applying machine learning techniques to genomic analysisProficiency with deep learning models for genomics such as AlphaFold, ESMFold, OpenFold, DeepSEA, and EnformerExperience with LLMs for genomic applications, including automated annotation and text‑based variant interpretationStrong programming skills in Python (preferred), R, or Julia, with experience using AI/ML libraries such as TensorFlow, PyTorch, or scikit‑learnExpertise in working with large‑scale genomic datasets, including WGS, WES, RNA‑seq, and methylation dataFamiliarity with cloud computing (GCP, AWS, or Azure) or HPC environmentsExperience leveraging federated learning and privacy‑preserving AI for genomic data sharingStrong interpersonal and communication skills—able to translate between technical, scientific, and clinical domainsStatistical and visualization skills for exploring –omics datasetsNice to HaveExperience with clinical genomics, ACMG variant interpretation, or diagnostic AI pipelinesExperience developing workflows using WDL, CWL, Nextflow, or SnakemakeFamiliarity with public genomics datasets (e.g., gnomAD, TCGA, ENCODE) and FAIR data principlesHands‑on experience with reinforcement learning and self‑supervised learning for genomics applicationsExperience applying zero‑shot or few‑shot learning for novel variant prediction and annotationBackground in causal inference and graph‑based AI for biological networksWhy Join DNAstackWork on meaningful projects that help improve patient outcomesBe part of a collaborative, mission‑driven team in a growing startupFlexible hours and remote‑friendly cultureCompetitive salary, benefits, and stock optionsOpportunity to contribute to open science and global data‑sharing effortsLet’s TalkIf this role sounds like a fit—and you’re excited to help shape the future of genomic medicine—we’d love to hear from you. Send us your resume and a short note about why you’re interested. #J-18808-Ljbffr