• Design, implement, and evaluate novel tools for the aggregation, analysis, and visualization of cancer data (mainly genomic and clinical data) based on various artificial intelligence approaches
• Identify pain points in the existing data pipeline and provide insight into methods to efficiently solve them
• Collaborate with computational biology and software engineering teams on the development of data analysis and storage pipelines (genomic data, transcriptomic data, epigenomic data, etc.) for the discovery of innovative therapeutic and diagnostic targets in oncology and immuno-oncology
• Collaborate with internal business development team on the identification of strategic R&D programs, and with external partners for the development of the company’s technology platform
• Contribute to the setup of the company’s IT/cloud systems and overall tech stack (cloud architecture, database, collaborative tools, etc.)
• Document, summarize, and present results in various settings, including management meetings and, on occasion, scientific conferences (domestically and internationally)

Minimum Requirements

• MS in predictive analytics, applied mathematics, statistics, computer science, artificial intelligence, or related technical fields
• Advanced skills in Python programming and the machine learning toolkits, such as Tensorflow, Pytorch, Scikit-Learn and etc.
• Experience with bash environment (Linux) and version control systems (Git)
• Strong English written and verbal, and interpersonal communication skills
• A team player with the ability to also work autonomously, and who has a lifelong learning mindset

Preferred Qualifications

• PhD or MS with 3+ years of experience in predictive analytics, applied mathematics, statistics, computer science, artificial intelligence, or related technical fields
• Demonstrated experience in all phases of managing data science projects including: problem definition, solution formulation, model building, productionizing and delivering measurable impact
• Prior experience involving the analysis of biomedical, healthcare, and/or oncology data
• Experience with cloud computing infrastructure and (AWS, Azure, OVH) and microservice/container systems (e.g. Docker)
• Experience with multi-threaded design and parallel/distributed computing
• Publication in ML/NLP conference (ICML, NeurIPS, ICLR, AAAI, ACL, EMNLP, EACL, COLING, etc.) or biomedical informatics/bioinformatics journals