Data Scientist, Cardiovascular modeling

Responsibilities

• Lead biostatistics efforts in a team setting of fellow scientists and machine learning engineers
• Cross-functional collaboration with other teams to integrate large-scale datasets and use bioinformatics to define the factors associated with drug response and resistance
• Develop computational methods and build tools to enable data-driven patient stratification and inform the development of clinical decision support products
• Identify scientifically relevant findings within datasets spanning multiple modalities
• Develop scalable computational solutions to further clinical data analysis automation across projects
• Communicate findings both internally and externally and recommend follow up actions

Qualifications

• Ph.D. in bioinformatics, engineering, statistics, biostatistics, biomedical engineering, cancer biology, immunology, genetics, or similar with 3+ years of industry experience or strong academic record.
• Demonstrated ability of contributing to multi-disciplinary team projects.
• Expertise with programming language such as R AND Python for complex data analysis and reproducible research practices.
• Mastery in integrating and analyzing diverse high-dimensional data sets relevant to oncology, immunology, and other areas of clinical relevance.
• Capability to conduct systematic analyses to evaluate multiple hypotheses and prioritize clinically-relevant questions.
• Scientific curiosity with an ability to identify questions that computational approaches can address, and the skills to develop solutions both independently and collaboratively.
• Excellent problem-solving and collaboration skills.
• Strong communication, data presentation, and visualization skills.
• Strong publication record.

• Experience in Cardiovascular modeling
• Timeseries data experience