JMP 2027 Summer Internship; Causal Inference (Master's/Phd)
About The Position
JMP, a subsidiary of SAS, is hiring a 2027 summer intern for its Life Sciences team. The internship focuses on developing causal inference workflows and methods and exploring advanced algorithms that could be adopted into JMP. Interns may work with R, Python, C++, or another suitable programming language, and may investigate areas such as causal discovery, causal machine learning, LLM-based methods, and other cutting-edge approaches. The work may include testing, benchmarking, documenting, and promoting results in written or video formats. The internship runs from May 18, 2027, through August 6, 2027, lasts 12 weeks, and requires interns to be available to start in person at the Cary headquarters on May 18; the dates are not flexible. The role uses a hybrid work approach, combining in-office collaboration with remote flexibility in alignment with the team’s needs. Applicants must be legally authorized to work in the United States and must not require employment visa sponsorship now or in the future.
Requirements
- Pursuing a Master’s degree or preferably a Ph.D. in Statistics, Biostatistics, Computer Science, Data Science, Economics, Engineering, or another life-science-analytics-focused major.
- Must not be graduating before December 2027.
- Strong knowledge of causal inference.
- Very strong written and verbal communication skills.
- Very strong coding skills.
- Demonstrated leadership, initiative, and willingness to go above and beyond.
- Interest in the future of analytics and openness to technology.
- Curious, passionate, authentic, and accountable.
- Available to start in person at Cary headquarters on May 18, 2027, and to work for the full 12-week internship through August 6, 2027; dates are not flexible.
- Legally authorized to work in the United States and not require employment visa sponsorship now or in the future.
Nice To Haves
- Ph.D. studies in a relevant field (preferred over a Master’s degree).
- Knowledge of individual treatment effect.
Responsibilities
- Program in R, Python, C++, or another suitable coding language to build causal inference workflows and methods.
- Explore algorithms that may be adopted into JMP, including causal discovery, causal machine learning, LLM-based methods, and other cutting-edge methods.
- Test, benchmark, document, and promote the work in written and/or video formats.
Benefits
- Access to company leaders and executives.
- Hybrid work flexibility, aligned with team needs.
- Free SAS programming training and certification.
- Well-being programs, including an on-site Work/Life Center, Recreation and Fitness Center, Employee Assistance Program (EAP), and subsidized cafés.
- Hackathons, social events, and other opportunities to connect and engage.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
