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hiring [2021/02/02 13:59] jfries created |
hiring [2021/02/09 16:01] jfries [Position 2: Machine Learning Engineer for release and enhancement of EHR representation learning software] |
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- | The Shah Lab is currently seeking students to fill the following RA positions. | + | ====== Open positions |
- | ===== Role: | + | Contact: Jason Fries, jfries@stanford.edu |
- | **Description**: | + | Please provide |
- | We are looking for a Machine Learning Engineer / Data Scientist to work on exciting challenges at the intersection of Machine Learning and Healthcare. This is a unique opportunity to be working on Machine Learning models deployed on live Electronic Health Record data which enable and support various hospital functions and clinical workflows impacting thousands of patients each day. | + | |
- | **Responsibilities: | + | |
+ | | ||
+ | | ||
- | * Understanding clinical requirements and translating them into technical problem statements. | + | ===== Position 1: Machine Learning Engineer for point of care model deployments ===== |
+ | |||
+ | **Description** \\ We are looking for a Machine Learning Engineer / Data Scientist to work on exciting challenges at the intersection of Machine Learning and Healthcare. This is a unique opportunity to be working on Machine Learning models deployed on live Electronic Health Record data which enable and support various hospital functions and clinical workflows impacting patient care each day. (see [[https:// | ||
+ | |||
+ | **Responsibilities** | ||
+ | |||
+ | | ||
* Communicating results and observations to technical audiences as well as clinicians in the form of visualizations, | * Communicating results and observations to technical audiences as well as clinicians in the form of visualizations, | ||
* Designing and implementing machine learning models to solve real world problems. | * Designing and implementing machine learning models to solve real world problems. | ||
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* Developing software that interacts with various hospital IT systems. | * Developing software that interacts with various hospital IT systems. | ||
- | **Requirements:** | + | **Requirements** |
- | * 5+ years of experience in software design and development. | + | * 3+ years of experience in software design and development. |
* 2+ years of hands-on experience using Python based machine learning libraries such as scikit-learn, | * 2+ years of hands-on experience using Python based machine learning libraries such as scikit-learn, | ||
* Experience working in a Linux environment and being comfortable with UNIX command line tools. | * Experience working in a Linux environment and being comfortable with UNIX command line tools. | ||
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* Prior experience with production deployment of software systems and/or machine learning systems. | * Prior experience with production deployment of software systems and/or machine learning systems. | ||
* Educational background involving quantitative techniques (CS, EE, Math, Statistics, etc.) | * Educational background involving quantitative techniques (CS, EE, Math, Statistics, etc.) | ||
+ | |||
+ | ---- | ||
+ | |||
+ | ===== Position 2: Machine Learning Engineer for release and enhancement of EHR representation learning software ===== | ||
+ | |||
+ | **Description** | ||
+ | |||
+ | Our team has built a state-of-the-art EHR representation learning technique named CLMBR. We are looking to recruit a research assistant (RA) to assist in developing publicly releasable code for broad use of CLMBR. | ||
+ | |||
+ | Deploying risk-stratification models in the clinic requires addressing questions about the robustness of large, pre-trained models, such as characterizing their reliance on memorization and spurious correlations, | ||
+ | |||
+ | The successful candidate for this RA position would be supervised by research scientists who are experts in representation learning, transfer learning and weak-supervision across multiple modalities of data. The RA will be responsible for implementing code for an open source API to enable rapid prototyping and evaluation of risk-stratification models built using CLMBR from Stanford' | ||
+ | |||
+ | **Research Focus Areas** | ||
+ | |||
+ | * Developing robustness evaluations of EHR-based representation models | ||
+ | * Contrastive learning with multi-modal EHR data (text + tabular data) | ||
+ | |||
+ | **Responsibilities** | ||
+ | * Discuss API needs with lab stakeholders and clinical collaborators | ||
+ | * Developing and documenting code for open source release | ||
+ | * Running experiments for clinical model evaluation. | ||
+ | * Communicating results and observations to technical audiences as well as clinicians in the form of visualizations, | ||
+ | |||
+ | **Required Skills** | ||
+ | |||
+ | * 3+ years of experience in software design and development. | ||
+ | * 2+ years of hands-on experience using Python based machine learning libraries such as scikit-learn, | ||
+ | * Strong communication skills and prior research experience required | ||
+ | * Experience working in a Linux environment and being comfortable with UNIX command line tools. | ||
+ | * Familiarity with productivity tools like Git, Docker. | ||
+ | |||
+ | **Preferred Skills** | ||
+ | |||
+ | * Familiarity with Google Cloud Platform (GCP) services such as BigQuery | ||
+ | * Prior experiment working with Stanford' | ||
+ | |||
+ | **Relevant Papers** | ||
+ | |||
+ | * CLMBR - Language models are an effective representation learning technique for electronic health record data [[https:// | ||