Sr. Associate Scientist-1

Overview

Support quantitative bioinformatics activities for biomarker research, including data analysis, data operations and quality control, visualization tools, pipeline automation, and cross-study data integration.

What you'll do

• Implement well-defined analytical requests, maintain data and results viewers, and support AI-assisted code development.
• Contribute to scalable data science solutions that accelerate biomarker research and decision-making.
• Support figure and analysis updates, including generating outputs with different subsets, groupings, labels, color palettes, and formatting while maintaining analytical integrity.
• Implement and optimize automated data processing pipelines, perform routine quality control checks, and document run parameters, versions, and deviations.
• Conduct data QC and results QC activities, generate validation summaries, and route identified issues for resolution.
• Refresh and maintain RMarkdown, Quarto, or Shiny-based data viewers for single-study and cross-study data exploration.
• Support cross-study database integration activities, including data collation, variable mapping, identifier harmonization, ETL validation, and data ingestion.
• Generate balanced Olink randomization layouts using approved algorithms and validate balance metrics before downstream execution.
• Maintain and enhance reusable AI-assisted coding libraries, prompts, snippets, and development standards for common analytical activities.
• Collaborate with scientists, statisticians, bioinformaticians, and cross-functional teams to ensure high-quality, reproducible analyses and data delivery.

What you'll need

• Bachelor's or Master's degree in Bioinformatics, Computational Biology, Statistics, Computer Science, Life Sciences, or a related quantitative field.
• Experience with open-source programming languages and tools such as R, R Shiny, Python, and Bash.
• Experience working with version control systems such as Git.
• Familiarity with AI-assisted development platforms such as Claude Code and other AI tools.
• Understanding of statistical modeling fundamentals, including linear models, logistic regression, survival analysis concepts, and multiple testing awareness.
• Strong attention to detail with a quality control and validation attitude.
• Excellent communication, collaboration, and problem-solving skills.
• Ability to work effectively in a fast-paced, collaborative research environment.

Nice to have

• Experience supporting biomarker, clinical, translational, or large-scale scientific datasets.

Details

• Location: Hyderabad.
• Work mode: Hybrid.
• Many roles are required to be performed on-site; speak with your recruiter and hiring manager about on-site expectations for this role and location.