Overview
Hybrid
Depends on Experience
Contract - W2
Contract - 12 Month(s)
50% Travel
Skills
Data Science
Health Care
Python
R
Machine Learning (ML)
Artificial Intelligence
Analytical Skill
Research
Management
Docker
Collaboration
Job Details
Please find the below direct client req
Data Scientist
On W2 Only (USC)
Arden Hills MN Hybrid
Healthcare domain exp is a plus
Machine learning, predictive modeling analysis
- Implement supervised and unsupervised machine learning algorithms, including regression, tree-based models, clustering, NLP, etc.
- Translate business requirements into technical specifications required for applying machine learning techniques.
- Implement machine learning techniques using modern analytic environments in Python and/or R.
- Research machine learning and AI techniques and technical proofs-of-concept to ensure optimal solutions. Leverage technologies such as Python, R, Docker, scripting, etc. to develop and deploy production-ready scripts for ongoing model scoring and training as appropriate.
Exploratory Data Analysis
- Perform data manipulation and munging, including data cleansing, transformations, integrations, missing value imputation, etc.
- Identify data sources appropriate to solve business problems, requiring expertise in data manipulation for data as large as billions of records across dozens of interconnected sources.
- Identify business use cases and specify data sources, analysis techniques, and quantitative outputs.
- Provide quantitative structure to business problems. Present results, make recommendations, and explain complicated mathematical ideas in simple terms for business partners.
Data Science Leadership
- Educate stakeholders on data science, analytics, and how it can solve business problems.
- Collaborate with other data scientists in other teams across the organization to brainstorm how to solve business problems.
- Oversee the work of intermediate and associate data scientists and teach and guide them to develop their skills.
- Participate in enterprise-wide initiatives to advance data science maturity
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