Data Analyst - Model Credibility

Job

NYU Grossman School of Medicine is one of the nation's top-ranked medical schools. For 175 years, NYU Grossman School of Medicine has trained thousands of physicians and scientists who have helped to shape the course of medical history and enrich the lives of countless people. An integral part of NYU Langone Health, the Grossman School of Medicine at its core is committed to improving the human condition through medical education, scientific research, and direct patient care.

At NYU Langone Health, equity and inclusion are fundamental values. We strive to be a place where our exceptionally talented faculty, staff, and students of all identities can thrive. We embrace inclusion and individual skills, ideas, and knowledge.

We have an exciting opportunity to join our team as a Data Analyst - Model Credibility. We are seeking a motivated Data Analyst to join our team and contribute to an exciting research program. As part of the recently launched Complement-ARIE program, the NYU-Sage New Approach Methodologies (NAMs) Data Hub and Coordinating Center will create a risk and credibility assessment program to work together with NAMs researchers to plan for and execute credibility assessments for their developed technologies.

The program will establish a framework for risk and credibility assessment.

The successful candidate is expected to support and/or lead the assessment of credible, reproducible, and regulatory-ready models. Responsibilities may include defining the models Context of Use (COU), scope, assumptions, limitations, risk profile, credibility goals, and acceptance criteria; assessing metadata completeness and reproducibility; and supporting verification, validation, uncertainty quantification, sensitivity analysis, robustness testing, and regulatory documentation.

• Assist in developing and implementing risk and credibility assessment frameworks for NAMs, including computational models, mechanistic experiments/simulations, organoid models, technologies in toxicology, and combinatory NAMs.
• Support context-of-use (COU) definition, applicability analysis
• Support/consult experimental designs of NAMs technology development
• Contribute to verification and validation (V&V) studies, including software testing, computational reproducibility, numerical consistency checks.
• Assist with statistical and computational analyses using experimental, simulation, omics, or clinical data.
• Uncertainty quantification, and robustness/sensitivity evaluations.
• Support development and maintenance of reproducible computational pipelines.
• Assist with preparation of technical and regulatory documentation, including validation reports, SOPs, audit-readiness documentation, and quality-management records.
• Establish pipeline QA/QC
• Support alignment with relevant regulatory agencies (FDA, EPA) and standards frameworks.

• Masters degree in a quantitative discipline (Biomedical Informatics, Computer Science, Machine Learning, Applied Statistics, Mathematics or similar field) and 3 years of experience in machine learning/data science.
• Proficiency in at least one programming language (Python, R) and machine learning tools (scikit-learn, R).
• Knowledge of predictive modeling and machine learning concepts, including design, development, evaluation, deployment and scaling to large datasets.
• Familiarity with computing models for big data Hadoop / Map Reduce, Spark etc.
• Knowledge of databases (Relational / SQL, NOSQL MongoDB etc.).
• Good grasp of software engineering principles. Experience in integrating modern software architectures.
• Knowledge and some experience in operational aspects of software development and deployment, including automation, testing, virtualization and container technology.
• Knowledge of clinical and operational aspects of healthcare delivery.
• Excellent written and oral communication skills for a variety of audiences.

• Experience with New Approach Methodologies (NAMs), computational toxicology, physiologically based pharmacokinetic (PBPK) modeling, systems biology, or mechanistic modeling.
• Familiarity with risk and credibility assessment…