Senior Data Scientist - AI & Genomics - Remote

Overview

An overview paragraph about the job

In Short

• 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.

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.

Benefits

• Work on meaningful projects that help improve patient outcomes.
• Be part of a collaborative, mission-driven team in a growing startup.
• Flexible hours and remote-friendly culture.
• Competitive salary, benefits, and stock options.
• Opportunity to contribute to open science and global data-sharing efforts.