LeadStack Inc. · 3 days ago
Associate Scientist - Bioinformatics
LeadStack Inc. is seeking a Bioinformatics Data Engineer to design and develop advanced AI-driven data ecosystems for drug target discovery. The role involves implementing scalable machine learning models and collaborating with interdisciplinary teams to translate experimental data into actionable strategies.
Responsibilities
Architect and Develop AI-Driven Ecosystems: Design and develop an advanced AI-driven data ecosystem to facilitate efficient and accurate drug target discovery
Implement Scalable Machine Learning Models: Design and implement scalable machine learning models and data pipelines to analyze complex biological datasets, including genomic, transcriptomic, and imaging data
Cloud Platform Utilization: Utilize modern cloud platforms to deploy machine learning solutions and manage large-scale data storage and computation
Advanced AI Techniques: Innovate and apply state-of-the-art AI techniques, including deep neural networks, to extract insights from large and diverse datasets
Collaborate on Drug Discovery Strategies: Work with interdisciplinary teams to translate experimental data into actionable drug discovery strategies
Continuous Technology Integration: Stay at the forefront of AI, machine learning, and bioinformatics, continuously integrating new technologies and methodologies to enhance data-driven decision-making
Qualification
Required
Experience in architecting and developing AI-driven data ecosystems
Proficiency in designing and implementing scalable machine learning models and data pipelines
Experience analyzing complex biological datasets, including genomic, transcriptomic, and imaging data
Familiarity with modern cloud platforms for deploying machine learning solutions and managing large-scale data storage and computation
Knowledge of advanced AI techniques, including deep neural networks
Ability to collaborate with interdisciplinary teams on drug discovery strategies
Commitment to continuous technology integration in AI, machine learning, and bioinformatics