Implement and optimize machine learning algorithms for a variety of healthcare problems using structured and unstructured data sources
Familiar with health and medical diagnosis codes
Create approach, methodology and work plan for healthcare problems.
Prepare raw data from health information systems, surveys and public domain for machine learning models.
Configure end-to-end components of a big data pipeline for various data science problems
Present findings of analyses and explain the models to clients
25% travel expected
Ideal Applicants Have
Infectious passion for data science, machine learning and data in general
The attitude of a positive team player. We really mean it.
Excellent written and verbal communication skills.
A genuine and demonstrated passion for data science, machine learning and data in general
Ability to learn quickly and prioritize effectively
Ability to impact and influence solution direction without authority, drive cross group collaboration, successfully advocate for customers and apply critical thinking and creative solutions in a dynamic highly ambiguous working environment.
Strong sense of pride and personal accountability for the end-to-end product/service quality, completeness, and resulting in good quality user experience
Self-starter with a proven ability to work in fast-paced environment; possesses a “whatever it takes mentality,” able to quickly and easily adapt to changing mandates and priorities.
The ability to demonstrate a sense of urgency around critical priorities, but work calmly, independently and effectively under pressure.
A master’s degree in Statistics, Computer Science, and Computer Engineering, or Economics is required
Applied knowledge of Python, Hive, Pig, Mahout, Java, C#
Substantial coursework in Data Management, Data Warehousing, Machine Learning, Statistical inference
Working knowledge of big data concepts like Hadoop, MapReduce, and HDFS
Familiarity with data tools and services in Azure, AWS, and/or GCP eco-system is required
2+ years of demonstrated experience using quantitative and qualitative data to make decisions and recommendations, to build and communicate plans, and to monitor and measure progress against goals.
Must have delivered outcomes in at least six figures in dollars leveraging data science practices.
Excellent communication and writing skills (English).