Data Science Analyst I-Personalized Medicine

Key Skills

CUDA/GPU programming, Data science techniques, Deep Learning, Design, KPIs, Machine learning techniques, matplotlib, Python Programming, PyTorch

Job Description

The Augmented Intelligence in Medicine and Science (AIMS) Laboratory is breaking new ground on a daily basis in applying data driven insights to improve the care of our patients. This highly innovated lab funded by a number of R01 and other NIH grants is seeking a highly qualified data scientist to help derive insights from real-world clinical data that can be used to improve clinical practice. The individual will be an active leader in a highly innovative lab applying cutting edge machine learning techniques to multi-modal electronic health record (EHR), waveform and genomic data to answer highly relevant clinical questions and deploy these models into clinical care.

Responsibilities

The clinical deep learning scientist will partner with an array of data savvy clinicians, researchers and students on multiple clinical research projects in the AIMS lab and broader Charles Bronfman Institute for Personalized Medicine (CBIPM). The AIMS lab has access to EHR data from millions of patients with a diverse range of backgrounds and clinical conditions using a variety of data science techniques and is part of the Mount Sinai Million Project that is actively enrolling and genotyping one million Mount Sinai Patients. The lab leverages state of the art infrastructure including a large on-premise high compute cluster with dedicated GPUs for the lab. This individual will have strong technical skills in the areas of data extraction and cleaning, deploying and developing advanced ML models, and interpreting the results of these tests.

Qualifications

• A Masters or PhD in data science (or equivalent in work experience). Previous experience with clinical data is preferred but not necessary
• Significant expertise with programming in Python, and machine learning using Pytorch, matplotlib or seaborn and related packages.
• Significant experience in using Linux in a shared, high-performance computing cluster environment, with a batch scheduler like SLURM or LSF.
• Expertise using machine learning techniques (i.e., random forests, gradient boosted trees, deep neural networks) for predictive modeling, including time series data, subtyping (supervised and unsupervised), reinforcement learning and other data science activities.
• Experience with natural language programming using a variety of techniques, text tokenization, including encoder-decoder neural network architectures, decoder-only architectures, learning embeddings, supervised finetuning using multiple GPUs. Experience with reinforcement learning with human feedback, prompt engineering and cloud APIs preferred but not necessary.
• Strong written and oral presentation skills including comfort with complex technical concepts to a variety of audiences.
• Experience with bio-statistical and epidemiological study designs and analytical frameworks is preferred.
• The candidate will be expected to develop familiarity with electronic health record (EHR) data, common data model platforms (e.g., OMOP) and clinical ontologies (e.g., SNOMED including ICD-9/10, LOINC, RxNorm, UMLS, CPT codes, etc.) on the job.

Employer Description

Strength Through Diversity

The Mount Sinai Health System believes that diversity, equity, and inclusion are key drivers for excellence. We share a common devotion to delivering exceptional patient care. When you join us, you become a part of Mount Sinai’s unrivaled record of achievement, education, and advancement as we revolutionize medicine together. We invite you to participate actively as a part of the Mount Sinai Health System team by:

• Using a lens of equity in all aspects of patient care delivery, education, and research to promote policies and practices to allow opportunities for all to thrive and reach their potential.
• Serving as a role model confronting racist, sexist, or other inappropriate actions by speaking up, challenging exclusionary organizational practices, and standing side-by-side in support of colleagues who experience discrimination.
• Inspiring and fostering an environment of anti-racist behaviors among and between departments and co-workers.

We work hard to acquire and retain the best people and to create an inclusive, welcoming and nurturing work environment where all feel they are valued, belong and are able to professional advance. We share the belief that all employees, regardless of job title or expertise contribute to the patient experience and quality of patient care.