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The AI Machine Learning Engineer bridges the gap between data science and implementation. They support data scientists and clinical informaticists in the development, deployment and maintenance of production-grade applications that leverage Machine Learning (ML) and Artificial Intelligence (AI). They partner with clinicians, subject matter experts, analytics developers, researchers, and data engineers to ensure that the delivered solutions provide actionable real-time insights that are in line with the systems clinical, financial and operational goals. They work closely with analytics leaders and business partners to bring the latest advances in ML and AI systems and processes to advance Childrens overall analytics.
Experience
- 2 years of experience in a Machine Learning Engineering, Data Science or Data Engineering role
- 1 year of experience in implementing machine learning algorithms in a production environment or applying software development lifecycle principles to analytics
Preferred Qualifications
- Experience developing software and data infrastructure to support the validation and operationalization of predictive models, including tools to support analysis, model. optimization, drift detection, model versioning, deployment and monitoring of model performance and features.
- Experience with model training and inference in common machine learning libraries and frameworks (scikit-learn, H2O, Tensorflow, PyTorch, Datarobot).
- Experience working with healthcare data (payer or provider) in a HIPAA regulated environment.
- Epic certification or badges in Cogito, Cognitive Computing Platform, Chronicles and Interconnect.
- Azure Certifications in Data Fundamentals, AI Fundamentals, Data Science or Data Engineering
Education
- Bachelors degree in Information Systems, Data Science, Machine Learning, Computer Science, Industrial Engineering, or Analytics.
- Masters degree in Information Systems, Data Science, Machine Learning, Computer Science, Industrial Engineering, or Analytics or four years of industry experience in lieu of Masters degree
Certification Summary
- No professional certifications required.
Knowledge, Skills and Abilities
- Familiarity with electronic health records (e.g., Epic, Cerner), ERP (e.g., Workday), or other business systems used at Childrens.
- Familiarity with cloud computing (Azure, AWS, Google Cloud) with focus on machine learning and data science tools (Databricks, Snowflake, Azure ML).
- Familiarity with leveraging open-source foundation large language models to build custom domain-specific applications.
- Working knowledge of machine learning concepts such as feature engineering, hyperparameter tuning, bias and variance tradeoff, model and data drift, and common metrics to evaluate model performance.
- Knowledge of data structure and data modelling principles and ability to efficiently ingest and process large datasets using Python, SQL, PySpark, and Scala.
- Knowledge of software development methodologies and architecture including API web services.
- Skilled at writing robust, production-grade code for application in predictive analytics (e.g.,Python, R, Java, Scala).
- Ability to reduce complexity and apply coding standards and best practices to data and machine learning pipelines, including code packaging, unit testing, data validation, containerization, automation, and CI/CD.
- Ability to create effective technical documentation and communicate requirements to business partners and vendors.
- Ability to work and provide coaching in agile, iterative frameworks, as well as develop in shared coding environments and collaborate with other developers using source control tools (i.e. Git).
Job Responsibilities
- Works closely with data scientists, data engineers and software engineers to develop and optimize reproducible and reusable pipelines for model training, validation, and inference.
- Monitors and troubleshoots AI and ML systems to ensure high availability, performance and reliability.
- Partners with subject matter experts and other non-technical stakeholders to understand their needs and effectively capture clinical and business system workflows when designing AI solutions.
- Develops and improves Childrens model development lifecycle by defining technical standards while reducing unnecessary complexity.
- Leads code reviews and debugging sessions as well as mentors and develops data scientists and analytics developers to improve code quality.
- Follows technical guidelines and best practices for project management, source control, documentation, and responsible and ethical AI.
- Collaborates with enterprise architects, analytics platform services and other technical leaders to create and maintain an up-to-date machine learning architecture roadmap.
- Partners with researchers on translational projects that are aligned with quality and operational goals, ensuring appropriate technical standards and regulatory compliance are followed.
- Contributes to organizational knowledge by participating in internal knowledge shares and seminars, research conferences, and online learning.
1575 Northeast Expy NE
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