Data Scientist I - Windreich Department for AI & Human Health Research

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.

• 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.

The Mount Sinai Health System (MSHS) provides salary ranges that comply with the New York City Law on Salary Transparency in Job Advertisements. The salary range for the role is $109,000.00 - $163,695.00 Annually. Actual salaries depend on a variety of factors, including experience, education, and operational need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits.

Non-Bargaining Unit, J18 - AI and Human Health - ISM, Icahn School of Medicine