Senior Data Scientist, Research

Cedars-Sinai

. Participate in biomedical research projects using programming, data mining, statistics, machine learning, and visualization techniques.

Posted 11/10/2026full-timeRemote • California • United StatesSenior💰 $117,100 - $198,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in biomedical data analysis through the development and application of algorithms and machine learning techniques. Proficient in programming with R and Python, and experienced in creating scalable AI/ML pipelines and database management.

Highest-signal resume keywords

Biomedical Data AnalysisMachine Learning DevelopmentR ProgrammingPython ProgrammingSQL Proficiency

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data MiningStatistical AnalysisAlgorithm DevelopmentData CleaningQuality ControlExploratory Data AnalysisPredictive ModelingComputational ToolsAI/ML Pipeline CreationDatabase Management

Soft Skills

CollaborationCommunicationLeadership

Tools & Technologies

GitHubNeo4jCITE-seqSpatial TranscriptomicsSpatial Proteomics

Industry Keywords

Biomedical ResearchHealthcarePharmaceutical IndustryClinical DataBiological Data

Tech Stack

Tools & technologies

Neo4jPythonSQL

About the role

Key responsibilities & impact

Participate in biomedical research projects using programming, data mining, statistics, machine learning, and visualization techniques.
Invent, develop, evaluate, and apply algorithms, methods, and software for biomedical data analysis.
Query databases and perform data cleaning, quality control, exploratory data analysis, and analytical methods.
Develop and implement AI-based analytical frameworks, predictive models, and computational tools for ALS, Alzheimer's disease, and Parkinson's disease research.
Integrate single-cell and spatial transcriptomics, CITE-seq, spatial proteomics, and other multimodal datasets.
Create database-to-deployment pipelines using R, Python, SQL, and neo4j.
Create sustainable data science infrastructure and scalable, reproducible AI/ML pipelines.
Write clean, performant, reusable code managed on GitHub; test and validate code and deploy models to multiple environments.
Lead communication of scientific findings through laboratory meetings, scientific conferences, peer-reviewed publications, grant applications, and new research initiatives.
Collaborate with data scientists, computational biologists, experimental researchers, senior research and lead research data scientists, and principal investigators.
Serve as a resource to 1–2 staff assisting with algorithm, method, and software development, evaluation, and application.
Provide feedback to the lead for appraisals relating to research projects and work performance.

Requirements

What you’ll need

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