Clinical Data Science Intern

Alcon Research • Fort Worth, TX

6d • Onsite

About The Position

Alcon is seeking a talented and motivated Clinical Data Science Intern to join our 2027 Summer Internship Program. The intern will contribute to a proof-of-concept project exploring the use of synthetic data generation compared with classical statistical resampling using Alcon’s historical clinical data. Through this internship, the student will gain meaningful industry experience applying modern data-generation techniques to real-world clinical data. The intern will build technical skills in statistical programming, data augmentation, and statistical inference while evaluating how synthetic data may complement traditional analytic methods and support more robust and reproducible clinical-data insights.

Requirements

• Must be enrolled in an accredited university or college for at least one semester prior to internship or co-op, and at least one additional semester following internship or co-op.
• Must be pursuing a M.S. or PhD in statistics, biostatistics, data science, or a related quantitative discipline.
• Working knowledge of R or Python programming.
• Minimum cumulative GPA 2.8/4.0.
• Demonstrated leadership skills, such as involvement in extracurricular activities, academic projects, research, or related experiences.
• Must be authorized to work in the U.S. on a permanent basis without requiring sponsorship. Students on an F-1 visa with CPT can be accommodated.
• Ability to commit to a 10–12-week, full-time internship of 40 hours per week.

Responsibilities

• Generate synthetic patient data using techniques such as generative adversarial networks (GANs) or Bayesian networks.
• Apply traditional statistical resampling approaches, including bootstrapping, to establish comparison benchmarks.
• Evaluate whether analyses augmented with synthetic data can replicate key statistical findings from the original real-world clinical dataset.
• Assess the fidelity, stability, reproducibility, benefits, and limitations of synthetic data compared with classical statistical resampling methods.
• Develop an implementation framework and application for synthetic data generation and summarize recommendations for potential future use with clinical data.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern