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

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.

About the Division
D3Ms 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.

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.

Minimum Qualifications
Bachelors degree in Computer Science Biomedical/Clinical Informatics Data Science Statistics Engineering or related field (Masters 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.
Preferred Qualifications (emphasis areas)
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.

Work Arrangement: Position is based in New York NY (Icahn School of Medicine at Mount Sinai). Hybrid flexibility may be available per departmental policy.