Kaiser Permanente is seeking a Data Scientist to support scoping, deploying, and reporting out on projects to support prospective risk adjustment projects, identifying and prioritizing prospective risk initiatives, and developing comprehensive reporting and insightful visualization of opportunities and outcomes.
Requirements
- Minimum three (3) years experience working with Exploratory Data Analysis (EDA) and visualization methods
- Minimum five (5) years machine learning and/or algorithmic experience
- Minimum five (5) years statistical analysis and modeling experience
- Minimum five (5) years programming experience
- Knowledge, Skills, and Abilities (KSAs): Strategic Thinking; Advanced Quantitative Data Modeling; Algorithms; Applied Data Analysis; Data Extraction; Data Visualization Tools
- Experience with SQL, Python, R, or other statistical modeling programs
- Familiarity with data science disciplines (i.e machine learning, predictive analytics, data visualization etc.)
Responsibilities
- Leads the development of detailed problem statements outlining hypotheses and their effect on target clients/customers
- Leads the design and development of data pipelines and automation for data acquisition and ingestion of raw data from multiple data sources and data formats
- Serves as an expert in the analysis and investigation of complex data sets
- Leads the selection, manipulation, and transformation of data into features used in machine learning algorithms
- Trains statistical models by selecting and leveraging algorithms and data mining techniques
- Leads the deployment and maintenance of reliable and efficient models through production
- Verifies and ensures model performance by demonstrating advanced expertise in the practice of a variety of model validation techniques
Other
- Bachelors degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Public Health, or related field
- Minimum eight (8) years experience in data science or a directly related field
- Travel: Yes, 5 % of the Time
- Employee Status: Regular
- Job Schedule: Full-time
