Senior Data Scientist

Summary

Join DNAstack, a leading precision medicine software company, as a Senior Genomics Data Scientist. You will develop and apply AI-driven data science techniques to analyze large-scale genomic data within a federated network. Responsibilities include designing, training, and validating machine learning models for genomics, collaborating with research and clinical partners, and utilizing federated learning frameworks. You will communicate complex analyses to various stakeholders and stay current with emerging AI techniques. This role requires a Master's degree or PhD in a related field and 4+ years of experience applying data science and machine learning to genomics datasets. DNAstack offers a flexible, remote-friendly culture, competitive salary and benefits, and stock options.

Requirements

• Master’s degree or PhD in data science, computational biology, bioinformatics, AI/ML, biostatistics, or a related field—or equivalent industry experience
• 4+ years of hands-on experience applying data science and machine learning techniques to genomics datasets
• Strong experience implementing AI-powered bioinformatics tools and applying machine learning techniques to genomic analysis
• Proficiency with deep learning models for genomics such as AlphaFold, ESMFold, OpenFold, DeepSEA, and Enformer
• Experience with LLMs for genomic applications, including automated annotation and text-based variant interpretation
• Strong programming skills in Python (preferred), R, or Julia, with experience using AI/ML libraries such as TensorFlow, PyTorch, or scikit-learn
• Expertise in working with large-scale genomic datasets, including WGS, WES, RNA-seq, and methylation data
• Familiarity with cloud computing (GCP, AWS, or Azure) or HPC environments
• Experience leveraging federated learning and privacy-preserving AI for genomic data sharing
• Strong interpersonal and communication skills—able to translate between technical, scientific, and clinical domains
• Statistical and visualization skills for exploring -omics datasets

Responsibilities

• Develop and apply AI-powered data science methods for analyzing large-scale genomic datasets in a federated environment
• Design, train, and validate machine learning and deep learning models for genomics, including variant classification, functional annotation, and disease risk prediction
• Collaborate with partners in academia and industry to create models that process and harmonize diverse genomics datasets
• Apply large language models (LLMs) and generative AI for automated annotation, scientific literature mining, and variant impact prediction
• Utilize federated learning frameworks to train AI models on distributed genomic data while ensuring privacy compliance
• Support the development of data harmonization and feature engineering pipelines to enhance AI model performance across genomic datasets
• Communicate complex AI-driven genomic analyses clearly to technical and non-technical stakeholders
• Stay current with emerging AI and deep learning techniques for genomics, participating in open-source initiatives and advancing industry best practices

Preferred Qualifications

• Experience with clinical genomics, ACMG variant interpretation, or diagnostic AI pipelines
• Experience developing workflows using WDL, CWL, Nextflow, or Snakemake
• Familiarity with public genomics datasets (e.g., gnomAD, TCGA, ENCODE) and FAIR data principles
• Hands-on experience with reinforcement learning and self-supervised learning for genomics applications
• Experience applying zero-shot or few-shot learning for novel variant prediction and annotation
• Background in causal inference and graph-based AI for biological networks

Benefits

• Flexible hours and remote-friendly culture
• Competitive salary, benefits, and stock options