Build scalable, production ready machine learning and statistical models to improve healthcare data latency through automation.
Requirements
- Advanced course in machine learning and programming.
- Experience with building, delivering and maintaining production ready machine learning models.
- Knowledge of statistical data analysis and machine learning such as linear models, time series forecasting, neural network, random forest and NLP models, etc.
- Expert in Python coding and utilization of machine learning and statistical packages for modeling.
- Experience with database skills, SQL, NoSQL, coding for ETL.
- In depth understanding of machine learning algorithms such as random forest, neural network, graph models, NLP, etc.
- Familiarity with Spark, Azure, Databricks, MLFlow AutoML.
Responsibilities
- Designs, develops and delivers statistical and/or machine learning models that solve business problems and work with engineers to make them production ready.
- Develops, utilize and monitor end-to-end machine learning pipeline from data ETL to model delivery for product ionization.
- Leads rapid prototyping for new business problems to support feasibility analysis for AI products.
- Builds and adopts solutions to automate and integrate data science processes.
- Researches latest and best solutions to solve data challenges at hand.
- Interprets and communicates results of complex models with cross functional team and the stakeholders.
- Work closely with the software engineering teams to drive scalable, production ready implementations.
Other
- Experience working in a heavily regulated industry. Healthcare is a plus.
- Experience working with global distributed multicultural teams.
- Experience with agile leadership.
- Ability to communicate and make recommendations to leadership.
- Excellent verbal and written communication skills, communicate complex findings in a clear and understandable manner
