LOCATION: 150 E 42ND STREET LOCATION. PREFER CANDIDATES THAT RESIDE IN TRI-STATE AREA NY, NJ, CT OR A COMMUTABLE DISTANCE TO MEET ONSITE/HYBRID REQUIREMENTS FOR THIS POSITION.

Data Scientist I will play a key role in Machine Learning Operations (MLOps), supporting the AI Assurance Team, responsible for validating and monitoring all AI products (both Generative AI and Non-Generative AI) that have been or will be deployed in the Mount Sinai Health System (MSHS) production environment. This role ensures that AI solutions meet governance, compliance, performance, and safety standards before and after clinical implementation.
This role will work closely with AI governance committee, product owners, DevOps engineers, Epic technical team, and clinicians to establish best practices in AI product deployment, validation, and monitoring. They will focus on ensuring AI models operate reliably, efficiently, and ethically within clinical workflows.

Responsibilities

• General ML Scientist responsibilities
• Data Curation: Collect, clean, and curate large and complex data sets from various sources to ensure it is suitable for machine learning tasks
• Product Development: Assist in the design, development, and training of machine learning application to solve specific optimization problems
• Model Evaluation: Assist in evaluating model performance and iteratively refine models based on feedback
• Deployment: Collaborate with the engineering team to execute appropriate QA process and deploy machine learning models into production environments
• AI Product Validation & Governance
• Validation of AI products (both Generative AI and Non-Generative AI) to ensure they meet governance and compliance standards before deployment in the MSHS production environment.
• Develop and implement testing frameworks to assess product accuracy, robustness, and fairness.
• Collaborate with primary developers and engineers to ensure products adhere to ethical guidelines, patient safety requirements, and regulatory standards.
• Ensure products maintain expected performance in clinical settings by tracking model drift, bias, and data integrity issues
• Stakeholder Communication & Documentation
• Communicate validation results and AI product performance insights to product owners, primary developers, engineers, clinical leaders, and AI governance committee members
• Maintain detailed documentation of AI assurance protocols, product evaluation procedures, and compliance measures
• Assist in drafting AI assurance reports and present the findings for leadership and AI governance committee
• Present high-level information to a diverse group of stakeholders
• Research: Stay up to date with the latest advancements in machine learning and AI to suggest improvements and innovative solutions
• Documentation: Maintain clear and organized documentation of data, models, and processes
• Effectively communicates statistical and technical ideas and results to non-technical stakeholders in written and verbal form to ensure decision points and their impact are clearly understood by all audiences
• Adheres to corporate standards for performance metrics, data collection, data integrity, query design, and reporting format to ensure high quality, meaningful analytic output.

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
• Proficiency in at least one programming language among Scala, Python, Java, C, or C++.
• Proficiency in database languages (e.g., SQL, NoSQL)
• Proficiency in cloud computing platforms (e.g., AWS, Azure, GCP)
• Familiarity with ML lifecycle management tools (e.g., MLflow, Kubeflow, Airflow)
• Experience with monitoring tools for AI model tracking
• Understanding of DevOps principles, CI/CD pipelines, and containerization (e.g., Docker, Kubernetes)
• Experience with version control systems (e.g., Git)
• Knowledge of big data technologies (e.g., Hadoop, Spark)
• Strong problem-solving skills and ability to work in cross-functional teams

• General ML Scientist responsibilities
• Data Curation: Collect, clean, and curate large and complex data sets from various sources to ensure it is suitable for machine learning tasks
• Product Development: Assist in the design, development, and training of machine learning application to solve specific optimization problems
• Model Evaluation: Assist in evaluating model performance and iteratively refine models based on feedback
• Deployment: Collaborate with the engineering team to execute appropriate QA process and deploy machine learning models into production environments
• AI Product Validation & Governance
• Validation of AI products (both Generative AI and Non-Generative AI) to ensure they meet governance and compliance standards before deployment in the MSHS production environment.
• Develop and implement testing frameworks to assess product accuracy, robustness, and fairness.
• Collaborate with primary developers and engineers to ensure products adhere to ethical guidelines, patient safety requirements, and regulatory standards.
• Ensure products maintain expected performance in clinical settings by tracking model drift, bias, and data integrity issues
• Stakeholder Communication & Documentation
• Communicate validation results and AI product performance insights to product owners, primary developers, engineers, clinical leaders, and AI governance committee members
• Maintain detailed documentation of AI assurance protocols, product evaluation procedures, and compliance measures
• Assist in drafting AI assurance reports and present the findings for leadership and AI governance committee
• Present high-level information to a diverse group of stakeholders
• Research: Stay up to date with the latest advancements in machine learning and AI to suggest improvements and innovative solutions
• Documentation: Maintain clear and organized documentation of data, models, and processes
• Effectively communicates statistical and technical ideas and results to non-technical stakeholders in written and verbal form to ensure decision points and their impact are clearly understood by all audiences
• Adheres to corporate standards for performance metrics, data collection, data integrity, query design, and reporting format to ensure high quality, meaningful analytic output