Job Description:
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. Specific activities include: 1.Providing guidance on study design to project teams. 2.Applying machine learning algorithms and other advanced analytic methods to drug discovery problems. 3.Exploring and understanding relevant datasets, both those generated internally and those from the public domain. 4.Communicating data analysis results and methods to the NIBR community and the broader scientific community. 5.Staying current with relevant computer science and statistical methods. 6.Educating colleagues about the strengths, weaknesses, and drug discovery potential of these analysis approaches. Minimum requirements:
Additional Info:
EEO Statement The Novartis Group of Companies are Equal Opportunity Employers and take pride in maintaining a diverse environment. We do not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status. [Click Here to Access the Original Job Post]
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. Specific activities include: 1.Providing guidance on study design to project teams. 2.Applying machine learning algorithms and other advanced analytic methods to drug discovery problems. 3.Exploring and understanding relevant datasets, both those generated internally and those from the public domain. 4.Communicating data analysis results and methods to the NIBR community and the broader scientific community. 5.Staying current with relevant computer science and statistical methods. 6.Educating colleagues about the strengths, weaknesses, and drug discovery potential of these analysis approaches. Minimum requirements:
- Ph.D. in computer science, computational biology, statistics or a related field
- 3+ years of data science or applied computer science experience
- Excellent interpersonal and communication skills
- Ability to work as part of a team
- Strong scientific curiosity and drive to make discoveries
- Expertise in machine learning, data analysis and statistics
- Experience working in Unix/Linux environment
- Interest and experience in life sciences preferred
- Industry experience preferred
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Additional Info:
EEO Statement The Novartis Group of Companies are Equal Opportunity Employers and take pride in maintaining a diverse environment. We do not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status. [Click Here to Access the Original Job Post]