Machine Learning & AI Analyst (Clinical Research) - Data Driven & Digital Medicine

Mount Sinai Health Systems • New York, NY

7d • $87,692 - $131,538 • Hybrid

About The Position

The Division of Data-Driven and Digital Medicine (D3M) is recruiting an early- to mid-career Machine Learning / AI Analyst to design, build, and evaluate solutions with a primary emphasis on Natural Language Processing (NLP) and multimodal AI, including multi-omics. Beyond clinical research and translation, the role includes developing internal decision-making and productivity tools for the Department of Medicine. You will collaborate with clinicians, scientists, and operations partners to turn clinical narratives, structured data, imaging, waveforms, and multi-omics into trustworthy models and user-friendly tools.
D3M's mission is to bring data-driven and digital innovation to research, education, and clinical care at Mount Sinai-accelerating the translation of AI and digital tools into practice while training the next generation of leaders. The Division collaborates broadly across the Health System to catalyze groundbreaking research and deploy real-world solutions.

Requirements

• Bachelor's degree in Computer Science, Biomedical/Clinical Informatics, Data Science, Statistics, Engineering, or related field (Master's preferred).
• 2+ years (industry, health system, or academic) working with ML/NLP using Python and/or R; strong SQL for data wrangling.
• Hands-on experience with modern ML/NLP (scikit-learn, PyTorch/TensorFlow; spaCy/Hugging Face), experiment tracking, and reproducible workflows.
• Ability to translate clinical/operational problems into analytical solutions and to communicate results to mixed audiences.
• Curiosity, product mindset, and commitment to responsible AI in healthcare.

Nice To Haves

• Deep experience in NLP and LLMs (prompting, fine-tuning, evaluation) and RAG over clinical knowledge bases.
• Multimodal learning across text, tabular, imaging, biosignals, and multi-omics.
• Experience integrating or analyzing multi-omics modalities (e.g., genomics, transcriptomics, proteomics, metabolomics) and linking them to clinical outcomes.
• Experience working with EHR data and standards (e.g., OMOP).
• MLOps tooling (MLflow, Weights & Biases), containerization/orchestration (Docker, Kubernetes), and cloud platforms.
• Practical understanding of model governance, fairness, and human-in-the-loop evaluation in healthcare.
• Track record delivering prototypes or products used by clinicians/researchers; publications or open-source contributions a plus.

Responsibilities

• Lead NLP and multimodal ML efforts across text (clinical notes), tabular EHR, imaging, biosignals, and multi-omics to solve high-impact clinical and operational problems.
• Prototype and iterate internal decision-support and productivity tools (e.g., workflow triage, quality improvement insights, operational dashboards).
• Build robust data pipelines and features; ensure data integrity, lineage, and reproducibility.
• Train, fine-tune, and evaluate models (traditional ML, deep learning, and LLM-based approaches, including retrieval-augmented generation).
• Partner with clinical and operations leaders to frame problems, define success criteria, and run pilots that demonstrate measurable value.
• Operationalize models with MLOps best practices (versioning, CI/CD, monitoring, governance) and documentation for safe, responsible use.
• Follow privacy, security, and compliance requirements (e.g., HIPAA) and contribute to model risk management and bias/impact assessments.
• Communicate findings to technical and non-technical audiences through clear write-ups, visualizations, and presentations.

Benefits

• Salary range for the role is $87692 - $131538 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level