DATA SCIENTIST - HEALTHCAREFULL-TIME / 8AM-5PM The Data Scientist has a deep understanding of big data and will help the Agile teams in building and enabling big data analytics solutions. Works with the Agile squad and the business users to continuously collect insight from maintenance data and enable digital use cases
Essential Duties & Functions: - Designs, develops, and implements end-to-end machine learning production pipelines (data exploration, sampling, training data generation, feature engineering, model building, and performance evaluation)
- Ensures that data pipelines are scalable, repeatable, and secure, and can serve multiple users within the company
- Enables big data and batch/real-time analytical solutions that leverage emerging technologies
- Collects, parses, manages, analyzes and visualizes large sets of data using multiple platforms
- Translates complex functional and technical requirements into detailed architecture, design, and high performing software
- Codes, tests, and documents new or modified data systems to create robust and scalable applications for data analytics
- Implements security and recovery tools and techniques as required
- Works with Data Science Lead and developers to make sure that all data solutions are consistent
- Ensures all automated processes preserve data by managing the alignment of data availability and integration processes
- Develops standards and processes for integration projects and initiatives
Other Duties & Functions: - Other leadership requirements of Premier Health at a Senior staff level
- Grow the digital capabilities of Premier Health and teach Agile methodologies
- Works multiple missions, journeys, and IT initiatives as assigned.
The above duties and responsibilities may be essential job functions subject to reasonable accommodations. All job requirements listed include the minimum knowledge, skills, and/or ability deemed necessary to perform the job proficiently. This job description is not to be constructed as an exhausted statement of duties, responsibilities, and requirements. Employees may be required to perform any other job-related instructions as requested by their supervisor, subject to reasonable accommodations. Education: Minimum Level of Education Required: Master's degree Additional requirements: Type of degree: Masters or PhD Area of study or major: Information Technology, Computer Science, or a relative quantitative discipline Preferred educational qualifications: Masters ExperienceMinimum Level of Experience Required: 5 - 7 years of job related experience Prior job title or occupational experience: Data Scientist, Big Data Engineer, Sr. Data Analyst Prior specific functional responsibilities: Minimum of Five years of experience in Data Science. Preferred experience: Experience in developing machine learning algorithms in Python. Understanding of high performance algorithms and R statistical software ; Experience in industry data science (e.g., machine learning, predictive maintenance); preferred Capability to architect highly scalable distributed systems, using different open source tools; Excellent problem solving, critical thinking, and communication skills; Demonstrated experience with agile or other rapid development methods; Demonstrated experience with object oriented design, coding and testing patterns as well as experience in engineering software platforms and largescale data; Experience in developing presentations and communications to be shared with internal and external stakeholders; Expert knowledge of data modeling and understanding of different data structures; Experience using big data batch and streaming tools Other experience requirements: Ability to concisely and effectively interact with key roles within the Agile team; brings a high-energy and passionate outlook to the job and can influence those around them; able to build a sense of trust and rapport that creates a comfortable & effective workplace; passion for innovation and can do attitude
Knowledge/Skills Designs, develops, and implements end-to-end machine learning production pipelines (data exploration, sampling, training data generation, feature engineering, model building, and performance evaluation. Translates complex functional and technical requirements into detailed architecture, design, and high performing software
