Extracts knowledge and insights from data in order to investigate complex business problems through a range of data preparation, modeling, analysis and/or visualization techniques, including predictive analysis, business intelligence, pattern recognition, operational effectiveness and/or economic forecasting.
Requirements
- 7+ years of coding experience using Python with expertise in solution and software development.
- 7+ years in DevOps/MLOps and a deep understanding of software engineering principles.
- 7+ years of experience in TensorFlow and PyTorch, with additional cloud platform familiarity.
- Proven ability to manage the production lifecycle of an MLOps pipeline.
- Deep understanding of model deployment and serving.
- Experience with orchestration and containerization methodologies.
- Familiarity with model serving tools such as MLflow, Databricks
Responsibilities
- Drives the development and implementation of advanced machine learning models and algorithms to solve complex healthcare problems, leveraging techniques such as predictive modeling, deep learning, and natural language processing.
- Collaborates with multiple departments, including data scientists, clinicians, and Information Technology (IT) professionals, to understand business requirements, define machine learning projects, and prioritize initiatives based on strategic objectives.
- Interfaces with stakeholders to define performance metrics and evaluation methodologies for machine learning models, contributing to rigorous testing, validation, and performance monitoring of models to ensure accuracy and reliability.
- Designs and implements scalable and efficient machine learning systems, including data pipelines, preprocessing, feature engineering, and model training, ensuring the quality and integrity of healthcare data used for analysis.
- Advises on the optimization and improvement of data pipelines, model training processes, and infrastructure to enhance efficiency, scalability, and performance of machine learning solutions.
- Consults on and presents technical findings, insights, and recommendations to both technical and non-technical stakeholders, contributing to the dissemination and application of machine learning insights in the healthcare industry.
- Ensures compliance with data privacy regulations, ethical guidelines, and industry standards in machine learning engineering, supporting the development of protocols and practices for model interpretability, fairness, and transparency.
Other
- Bachelor degree from accredited university or equivalent work experience (HS diploma + 4 years relevant experience)
- Manages team performance through regular, timely feedback as well as the formal performance review process to ensure delivery of exceptional services and engagement, motivation, and team development.
- Stays up-to-date with the latest advancements in machine learning and related technologies, continuously exploring and evaluating new algorithms and methodologies to enhance machine learning capabilities in healthcare applications.
- Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.
