Intern, Data Science (Summer 2025)

The role: BigHat Biosciences is seeking a highly motivated Data Scientist intern to join us for Summer 2025. This position will focus on advancing BigHat’s data science capabilities, which support our state-of-the-art antibody engineering platform and therapeutic programs.

Possible projects include:

• Implement robust, scalable data processing workflows for transforming high-throughput experimental measurements into biologically-relevant datasets for statistical and machine learning
• Build interactive dashboards which integrate data across multiple assays and enable scientists and program managers to understand the status of each optimization campaign and improve antibody design
• Develop analyses and visualizations that relate high-dimensional antibody sequence space to experimental metrics of antibody function and quality
• Design and implement innovative strategies to analyze, model, and interpret diverse biological datasets
• Source, implement and improve state of the art computational approaches from the literature and public domain to accelerate BigHat’s antibody optimization campaigns
• Collaborate closely with a large cross-section of the BigHat team including wet lab scientists, automation engineers, software engineers, data scientists, and machine learning researchers

Preferred qualifications:

• Currently have or are working towards a bachelor’s or graduate degree (MS or PhD) in biology, statistics, computer science, bioengineering, or a related field
• Familiarity with bioinformatics pipelines, classic statistical models (regression, ANOVA, random effects models), experimental design, and AI/ML techniques (SVMs, deep learning)
• Competency in Python, R, or similar programming languages. Familiarity with pandas, git-based version control
• Enjoys a fast-paced environment where analyses are quickly translated into business and scientific decisions
• Demonstration of skills through publications or previous research/internship experience
• Nice-to-haves: experience communicating high-level results to scientific audiences, such as in lab/department meetings or conferences