<p>Description We are seeking a skilled Data Scientist I to join our team in the SinAI Assurance Lab. The position will play a key role in Machine Learning Operations and be responsible for validation, monitoring, and governance of AI models across clinical workflows at the Mount Sinai Health System. This role will ensure that these models meet Mount Sinai's high standards for safety, equity, and real-world performance. You will work in partnership with the AI Governance Committee, product owners, clinicians, Epic technical team, and DevOps teams to rigorously evaluate both Generative and Non-Generative AI tools before and after deployment. Responsibilities Core ML & Validation Curate, clean, and manage large complex datasets from various sources for modeling and analysis. Assist in designing, evaluating, and refining ML models, including performance benchmarking and fairness analysis. Develop and run model validation tests for robustness, accuracy, and generalizability across demographic subgroups. Collaborate on model QA and deployment workflows with engineering teams, ensuring models are safe and production ready. AI Product Governance Design validation protocols for AI models to ensure ethical use, bias mitigation, and regulatory compliance. Monitor deployed models for performance degradation, drift, and bias over time. Ensure auditability of all validation and monitoring outputs using standardized documentation practices. Assist in compliance workflows including traceability, re-validation, and version documentation of AI tools. Ensure products maintain expected performance in clinical settings by tracking model drift, bias, and data integrity issues Communication & Reporting Translate statistical and technical insights into accessible reports for clinicians, product managers, and governance bodies. Present results from AI validation testing and monitoring to stakeholders and contribute to institutional governance decisions. Maintain rigorous documentation on model performance, validation methods, and compliance actions. Stay current with emerging best practices in AI model development, validation, safety, transparency, and interpretability. 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++. Expert knowledge on Machine Learning Algorithms Proficiency in database languages (e.g., SQL, NoSQL) and cloud computing platforms (e.g., AWS, Azure, GCP) Proficiency in visualization tools like Plotly, Tableau, Power BI 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 Employer Description Strength through Unity and Inclusion The Mount Sinai Health System is committed to fostering an environment where everyone can contribute to excellence. We share a common dedication to delivering outstanding patient care. When you join us, you become part of Mount Sinai's unparalleled legacy of achievement, education, and innovation as we work together to transform healthcare. We encourage all team members to actively participate in creating a culture that ensures fair access to opportunities, promotes inclusive practices, and supports the success of every individual. At Mount Sinai, our leaders are committed to fostering a workplace where all employees feel valued, respected, and empowered to grow. We strive to create an environment where collaboration, fairness, and continuous learning drive positive change, improving the well-being of our staff, patients, and organization. Our leaders are expected to challenge outdated practices, promote a culture of respect, and work toward meaningful improvements that enhance patient care and workplace experiences. We are dedicated to building a supportive and welcoming environment where everyone has the opportunity to thrive and advance professionally. Explore this opportunity and be part of the next chapter in our history. About the Mount Sinai Health System: Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with more than 48,000 employees working across eight hospitals, more than 400 outpatient practices, more than 300 labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time - discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it. Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients' medical and emotional needs at the center of all treatment. The Health System includes more than 9,000 primary and specialty care physicians; 13 joint-venture outpatient surgery centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and more than 30 affiliated community health centers. We are consistently ranked by U.S. News & World Report's Best Hospitals, receiving high "Honor Roll" status, and are highly ranked: No. 1 in Geriatrics, top 5 in Cardiology/Heart Surgery, and top 20 in Diabetes/Endocrinology, Gastroenterology/GI Surgery, Neurology/Neurosurgery, Orthopedics, Pulmonology/Lung Surgery, Rehabilitation, and Urology. New York Eye and Ear Infirmary of Mount Sinai is ranked No. 12 in Ophthalmology. U.S. News & World Report's "Best Children's Hospitals" ranks Mount Sinai Kravis Children's Hospital among the country's best in several pediatric specialties. The Icahn School of Medicine at Mount Sinai is ranked No. 11 nationwide in National Institutes of Health funding and in the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges. Newsweek's "The World's Best Smart Hospitals" ranks The Mount Sinai Hospital as No. 1 in New York and in the top five globally, and Mount Sinai Morningside in the top 20 globally. Equal Opportunity Employer The Mount Sinai Health System is an equal opportunity employer, complying with all applicable federal civil rights laws. We do not discriminate, exclude, or treat individuals differently based on race, color, national origin, age, religion, disability, sex, sexual orientation, gender, veteran status, or any other characteristic protected by law. We are deeply committed to fostering an environment where all faculty, staff, students, trainees, patients, visitors, and the communities we serve feel respected and supported. Our goal is to create a healthcare and learning institution that actively works to remove barriers, address challenges, and promote fairness in all aspects of our organization. Compensation 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 $109000 - $163695 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.</p> <p>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++. Expert knowledge on Machine Learning Algorithms Proficiency in database languages (e.g., SQL, NoSQL) and cloud computing platforms (e.g., AWS, Azure, GCP) Proficiency in visualization tools like Plotly, Tableau, Power BI 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</p> <p>Core ML & Validation Curate, clean, and manage large complex datasets from various sources for modeling and analysis. Assist in designing, evaluating, and refining ML models, including performance benchmarking and fairness analysis. Develop and run model validation tests for robustness, accuracy, and generalizability across demographic subgroups. Collaborate on model QA and deployment workflows with engineering teams, ensuring models are safe and production ready. AI Product Governance Design validation protocols for AI models to ensure ethical use, bias mitigation, and regulatory compliance. Monitor deployed models for performance degradation, drift, and bias over time. Ensure auditability of all validation and monitoring outputs using standardized documentation practices. Assist in compliance workflows including traceability, re-validation, and version documentation of AI tools. Ensure products maintain expected performance in clinical settings by tracking model drift, bias, and data integrity issues Communication & Reporting Translate statistical and technical insights into accessible reports for clinicians, product managers, and governance bodies. Present results from AI validation testing and monitoring to stakeholders and contribute to institutional governance decisions. Maintain rigorous documentation on model performance, validation methods, and compliance actions. Stay current with emerging best practices in AI model development, validation, safety, transparency, and interpretability.</p>