Description:

DNAstack is seeking an experienced Senior Genomics Data Scientist to join their bioinformatics and AI team.
The role focuses on developing and applying AI-driven data science techniques to support bioinformatics and tertiary workflows across a federated network.
Responsibilities include designing, optimizing, and maintaining AI-powered analytical methods to extract insights from large-scale genomic data.
The candidate will work independently and with research and clinical partners, applying knowledge of genomics, machine learning, and data science best practices.
Key tasks involve developing AI-powered data science methods, designing and validating machine learning models, collaborating with partners, applying large language models, utilizing federated learning frameworks, and communicating complex analyses to stakeholders.
The position requires staying current with emerging AI and deep learning techniques for genomics.

Requirements:

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

Employees will work on meaningful projects that help improve patient outcomes.
The position offers a collaborative, mission-driven team environment in a growing startup.
Flexible hours and a remote-friendly culture are provided.
Competitive salary, benefits, and stock options are included.
There is an opportunity to contribute to open science and global data-sharing efforts.