Master's degree in a quantitative discipline or equivalent practical experience.
2 years of experience in data science, software engineering, or data analysis.
Proficiency in at least one programming language (Scala, Python, Java, C, or C++).
Experience with database languages (SQL, NoSQL).
Familiarity with cloud computing platforms (AWS, Azure, GCP).
Knowledge of big data technologies (Hadoop, Spark).
Self-motivated with the ability to work independently.
Responsibilities
Build and curate analytic cohorts from various data sources, ensuring quality checks for consistency and plausibility.
Develop and validate prediction models with uncertainty quantification, reporting on discrimination and calibration.
Implement and compare models across institutions and subgroups, addressing underperformance.
Apply causal inference methods to identify sub-phenotypes and personalized prevention strategies.
Maintain reproducible code and documentation for external replication and public release.
Contribute to manuscripts and presentations, clearly communicating methods and results.
Coordinate with site teams on data requests and project milestones.
Benefits
Opportunity to work on a high-impact, NHLBI-funded project.
Collaboration with leading institutions like Mount Sinai, UCLA, and Vanderbilt.
Access to cutting-edge machine learning techniques and methodologies.
Potential for professional growth through publications and presentations.
Engagement in a dynamic and innovative research environment.
Full Job Description
Job Description
The Data Scientist will join the PREVENT pAKI project (NHLBI-funded), which uses federated and pooled machine learning on EHR data from 15 hospitals and more than 320,000 non-cardiac surgery patients. The aims are to predict postoperative acute kidney injury (pAKI) and to learn personalized prevention strategies that generalize across patient subgroups. You will work directly with the PI, a nephrologist co-investigator, and site teams at Mount Sinai, UCLA and Vanderbilt. The role will leverage and create cutting edge machine learning techniques and emphasize statistical rigor, reproducibility and scientific publication.
Ideal candidate is a Master's graduate who will use their 2+ years of background experience in healthcare data science/analytics to successfully build cohorts of pAKI patients and develop deep learning models to predict pAKI risk in future patients. This candidate is comfortable with independently performing statistical analyses, can communicate effectively and collaborate well with project stakeholders/peers. Within 1 year, candidate will have deployed a successful deep learning model using PyTorch and be able to demonstrate performance of outcome prediction suitably for technical and non-technical audiences.
Responsibilities
Build and curate analytic cohorts from OMOP-mapped and perioperative data warehouse sources, including AKI outcome definitions (AKIN/KDIGO) and quality checks for consistency, plausibility and missingness.
Develop and validate prediction models (gradient-boosted trees, graph neural networks) with uncertainty quantification, and report discrimination, precision-recall and calibration.
Implement and compare site-specific, pooled and federated (stacked) models across three institutions and various subgroups and implement mitigation strategies for underperforming groups.
Apply causal inference methods (causal forests, policytree) and reinforcement learning to identify sub-phenotypes and personalized prevention strategies.
Maintain version-controlled, reproducible code and documentation sufficient for external replication, and prepare code and models for public release.
Contribute to manuscripts, abstracts and presentations, and communicate methods and results clearly to clinicians and non-technical audiences.
Coordinate with site teams on data requests, local model training, and project milestones.
Stay current with federated learning, causal ML and algorithmic fairness literature and propose methodological improvements.
Other duties as assigned.
Qualifications
Master's degree in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Information Technology, Mathematics, Physics) or equivalent practical experience.
2 years of work experience in data science, software engineering, or data analysis
Experience with at least one programming language among Scala, Python, Java, C, or C++.
Experience with database languages (e.g., SQL, NoSQL)
Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP)
Experience with version control systems (e.g., Git)
Knowledge of big data technologies (e.g., Hadoop, Spark)
Self-motivated with a demonstrated ability to work independently, and to exercise independent judgment in developing complex techniques or programs in a dynamic environment
Preferred:
Experience with EHR data (OMOP preferred) and clinical prediction modeling, including calibration and external validation.
Causal inference or heterogeneous treatment effect methods (e.g., grf/policytree).
Deep learning (PyTorch, graph neural networks), federated learning, or algorithmic fairness evaluation.
Cloud or HPC experience (AWS, Azure, GCP); PhD; first-author publications.
About Mount Sinai Hospital
Mount Sinai Hospital is a hospital network based in New York City. It was founded in 1852 and is one of the oldest and largest teaching hospitals in the United States. The hospital has been ranked among the top hospitals in the country by U.S. News & World Report and is known for its excellence in patient care, research, and education. Mount Sinai Hospital is affiliated with the Icahn School of Medicine at Mount Sinai and has a staff of over 7,000 physicians, nurses, and other healthcare professionals.
