Research Data Scientist

Title: Research Data Scientist

Location: Onsite, Pittsburgh, PA 15213

Type: Contract to Hire

Hours: Standard business hours

Overview:

Join a cutting-edge lab to discover novel therapeutics who are seeking a Research Data Scientist to provide advanced analytical and computational support for multidisciplinary biomedical research. This role will develop data pipelines, statistical models, and AI/machine learning approaches to analyze and integrate complex biological datasets, including multi-OMICs, imaging, virology, and other multimodal data.

Responsibilities:

• Develop scalable, reproducible data analysis pipelines for complex biological datasets.

• Apply statistical modeling, machine learning, and AI for predictive analytics and pattern recognition.

• Integrate and analyze multi-OMICs, imaging, molecular, cellular, and other multimodal data.

• Partner with investigators on study design, data strategy, and analytical approaches.

• Maintain data quality, preprocessing, version control, and standardized workflows.

• Create visualizations, dashboards, reports, and analytical summaries.

• Support data infrastructure and collaborate across biology, engineering, and computational teams.

Requirements:

• Master’s degree in Bioinformatics, Biostatistics, Computational Biology, Data Science, Statistics, Computer Science, or related field; Ph.D. preferred.

• 3+ years of relevant data analysis, statistical modeling, or computational research experience.

• Strong proficiency in Python and/or R and statistical analysis.

• Experience with large, complex datasets and reproducible computational pipelines.

• Strong analytical, problem-solving, communication, and collaboration skills.

Preferred Qualifications:

• Experience with multi-OMICs integration, machine learning/AI, advanced modeling, or quantitative imaging.

• Familiarity with cloud/HPC/big-data environments and data visualization.

• Biomedical or translational research experience.

• Experience contributing to scientific publications, grants, technical reports, or regulatory-aligned data frameworks.

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Ref: #558-Scientific