Data Science & Analytics Intern

Key Skills

Bayesian networks, Cloud computing, Data Analytics, Data Classification, Data science techniques, Data Visualization, Databricks, Deep Learning, Intellectually curious, creative, and diligent, Jupyter Notebook, Machine learning techniques, Predictive Modeling, PyMC3, Python Programming, PyTorch, Scikit-learn, TensorFlow

Job Description

Responsibilities

• Develop, train, and validate novel machine learning algorithms for prediction and classification
• Write detailed report describing applied algorithms and findings
• Present work at team meeting
• Participate in team/dept meetings to observe as well as present their work/learning
• Gain understanding of drug development process while being exposed to various functions within and outside of Data Science.

Qualifications

What you’ll bring to the role

• Strong knowledge and understanding of machine learning algorithms, predictive modeling, and classification.
• Strong programming skills in Python
• Experience with machine learning, data analytics and visualization tools (e.g. Scikit-learn, Tensorflow, Pytorch, Pymc, Jupyter...etc.)
• Ability to work well in teams, and present ideas clearly and concisely.
• Embrace our core values: Put People First, Improve Lives, Cultivate Curiosity, Do Right, Forge New Pathways

Desirable

• Good knowledge of Bayesian deep learning algorithms
• Experience in analysis of health care data
• Experience working in cloud environments (e.g. Databricks)

All Sage new hires will be required to provide documentation that they are fully vaccinated against COVID-19 or, if not, that they are legally entitled to an accommodation due to a medical condition or a sincerely held religious belief. Requests for accommodation will be considered on a case-by-case basis, and Sage will consider, among other things, whether the proposed accommodation would create an undue hardship.

All qualified applicants will receive consideration for employment without discrimination on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other factors prohibited by law.