Job Description

Join us as we translate today's discoveries into tomorrow's medicine!

Are you ready to be a part of breakthrough research?

About the Ajami Lab:

The Ajami Lab in the Department of Neurology at Cedars-Sinai investigates the role of microglia and peripheral immune cells in neurodegenerative diseases, including ALS, Alzheimer's disease, and Parkinson's disease. Our research combines advanced experimental models, single-cell and spatial multiomics, computational biology, and artificial intelligence to understand disease mechanisms and identify novel therapeutic targets.

Role of the Research Senior Data Scientist:

The incumbent participates in biomedical research projects using programming, data mining, statistics, machine learning, and visualization techniques to invent new algorithms, methods, and software for data analysis. Responsibilities include querying databases, data cleaning and quality control, supervised and unsupervised machine learning, parametric and nonparametric statistical methods, deploying, production models, and leading the communication of scientific findings via peer-reviewed publications and scientific conferences. Writes clean, performant, reusable code managed on GitHub to perform repeatable analyses and to train and deploy models to multiple environments. Serves as a resource 1-2 staff to assist with the development, evaluation, and/or application of algorithms, methods, and software for data analysis.

The Research Senior Data Scientist will play a key role in establishing and advancing an AI-driven computational research platform within the Ajami Lab. Working closely with other data scientists, computational biologists, and experimental researchers in the laboratory, this individual will develop and implement advanced artificial intelligence and machine learning approaches to integrate and interpret complex, high-dimensional biological datasets.

The position will focus on developing AI-based analytical frameworks, predictive models, and computational tools to uncover disease-associated cellular states, identify molecular pathways and therapeutic targets, and generate testable biological hypotheses across our ALS, Alzheimer's disease, and Parkinson's disease research programs.

The role will also involve integrating single-cell and spatial transcriptomics, CITE-seq, spatial proteomics, and other multimodal datasets; developing scalable and reproducible AI/ML pipelines; and collaborating with other scientists on scientific publications, grant applications, and new research initiatives.

Primary Duties & Responsibilities:

• Invents, develops, evaluates, and applies computational and statistical methods including artificial intelligence and machine learning algorithms and software for the analysis of biomedical data.
• Creates database-to-deployment pipelines for models using necessary programming languages (primarily R, Python, SQL, neo4j).
• Creates sustainable data science infrastructure and adheres to data analysis standard methodologies.
• Leads with the presentation and communication of scientific results through laboratory meetings, scientific conferences, and peer-reviewed publications.
• Performs data cleaning, quality control, and analytical methods.
• Performs exploratory data analysis to gauge the need for or appropriateness of advanced analytical methods.
• Leads discussions with senior research and/or lead research data scientists and principal investigators to identify areas where data science can best be applied to answer biomedical research questions.
• Tests and validates code to ensure adaptability of data applications with version control through GitHub.
• Provides feedback to the lead for appraisals relating to research projects and work performance.

Qualifications

Education:

• Bachelor's degree in Computer Sciences, Machine Learning, Applied Mathematics, Econometrics, Statistics, Engineering, Physics, or related field, required.

Experience and Skills:

• Minimum of 5 years of professional experience in data science, healthcare, or pharmaceutical industries or universities working with biomedical, clinical, or biological data.
• Experience programming at an intermediate or advanced skill level with a high-level programming language such as R or Python.
• Experience programming at an intermediate proficiency level in SQL.
• Experience with the development and evaluation of computational and/or statistical methods including machine learning for data analysis.
• Work experience should include being an author or co-author on one or more peer-reviewed scientific papers published in journals.