Machine Learning Engineer

Overview:

We are seeking a Bioinformatics Data Engineer to spearhead the development of an AI-first ecosystem aimed at transforming the drug discovery landscape. This role demands a proactive individual with a profound understanding of machine learning, AI, and bioinformatics, poised to deliver innovative solutions that accelerate the validation and identification of novel drugs.

The ideal candidate will have extensive experience in managing and analyzing large-scale biological data, understanding its intricacies, and using this data to train robust machine learning models.

Key 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.

Minimum Qualifications:

· Educational Background: PhD in Computer Science, Bioinformatics, Statistics, or a related quantitative field.

· Machine Learning Expertise: Solid experience with AI and machine learning tools and frameworks.

· Programming Proficiency: Proficient in Python and R, with a strong track record of developing and deploying applications in a cloud environment.

· Experience with Biological Datasets: Demonstrated ability in handling complex biological datasets, including consortium and atlas-type data, and applying advanced statistical and machine learning methods to solve real-world problems.

Preferred Skills:

· Data Handling Expertise: Extensive experience in data harmonization, normalization, and preprocessing of large-scale biological datasets to ensure their readiness for machine learning applications.

· Understanding of Biological Data Nuances: Deep understanding of the nature, limitations, and pitfalls of biological datasets and the ability to apply this knowledge systematically in data cleaning and management.

· Advanced Machine Learning Techniques: Expertise in genetic algorithms, ensemble methods, and unsupervised learning techniques, with a focus on their application to biological data.

· Analytical Skills: Excellent analytical skills, with the ability to see beyond the numbers to the strategic implications of the data.

· Passion for Innovation: A strong passion for using AI to drive innovations in health technology and drug discovery.