| || As a member of the Scientific Data Analysis team, you will explore and develop machine learning and other advanced analysis approaches for drug discovery applications. You will identify tractable machine learning problems in all stages of drug discovery from understanding basic disease biology, to target identification, assay development, safety, and patient stratification. Working with drug discovery teams and computational biology colleagues, you will frame key questions, define appropriate data sets, and perform data analysis on these projects. You will be a part of teams that bring together leading bench scientists, software engineers, statisticians and bioinformaticians to make sense of large scale experimental data. A successful candidate must demonstrate the ability to work collaboratively, communicate well, and to deliver actionable results.
In this role you will:
1. Provide guidance on study design to drug discovery project teams.
2. Answer biological questions using machine learning algorithms and other advanced analytic methods.
3. Explore relevant datasets, both those generated internally and those from the public domain.
4. Communicate data analysis results and methods to the NIBR community and the broader scientific community.
5. Stay current with relevant computer science and statistical methods.
6. Share your data analysis knowledge with colleagues.