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# Director, Statistical Innovation and Data Science

- **Company:** Aspiriamo
- **Location:** Fort Worth, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Data Integrity, Generalized Linear Model, Python (Programming Language), Machine Learning, Deep Learning, Model Validation, Performance Monitor, Data Management
- **Published:** October 20, 2026
- **Apply:** https://alcon.wd5.myworkdayjobs.com/careers_alcon/job/Fort-Worth-Texas/Director--Statistical-Innovation--Data-Science---AI_R-2026-50118-1

## About the Role

* Advanced degree (MSc or PhD) in Statistics or Biostatistics preferred, PhD in closely related quantitative disciplines may be considered.
 * Deep expertise in statistical inference and modeling, with demonstrated experience in linear and nonlinear modeling and other advanced statistical methods.
 * Applied expertise in data science, machine learning, and/or deep learning, preferably using clinical, biomedical, imaging, digital-health, or other high-dimensional data.
 * Proficiency in R and/or Python and experience with reproducible, version-controlled analytical workflows and modern statistical/ML computing environments.
 * Demonstrated ability to determine when classical statistical models, ML, deep learning, or hybrid approaches are most appropriate for a scientific question.
 * Experience supporting clinical development, regulatory submissions or interactions, and scientific publications; knowledge of model validation, data integrity, and evolving expectations for AI/ML in regulated environments.

What You'll Bring To Alcon:

 * Bachelor's Degree or Equivalent years of directly related experience (or high school +18 yrs; Assoc.+14 yrs; M.S.+7 yrs; PhD+6 yrs)
 * The ability to fluently read, write, understand and communicate in English
 * 10 Years of Relevant Experience
 * 6 Years of Demonstrated Leadership
 * undefined

## Description

* Develop and apply advanced statistical models, including linear and nonlinear models, generalized linear models, mixed-effects and hierarchical models, longitudinal and survival models, Bayesian methods, causal inference, and simulation.
 * Use statistical modeling to characterize treatment response, disease progression, heterogeneity, prediction, prognosis, benefit-risk, and relationships among clinical, imaging, digital, patient-reported, and real-world data.
 * Lead application of data science and ML methods for prediction, classification, clustering, patient stratification, signal identification, and high-dimensional data analysis.
 * Develop and evaluate deep-learning approaches, including applications to ophthalmic imaging and multimodal data integrating images with clinical and other complex data sources.
 * Compare classical statistical models, ML, and deep-learning approaches based on the scientific objective, assumptions, uncertainty, interpretability, validation, generalizability, and intended use rather than technological novelty.
 * Establish rigorous model-development and validation practices covering training, tuning, calibration, robustness, reproducibility, bias, interpretability, uncertainty, and performance monitoring as appropriate to context of use.
 * Identify and evaluate emerging approaches, including generative AI, and translate high-value prototypes into reusable analytical capabilities with appropriate human oversight and governance.
 * Partner across Statistics, Clinical Development, Regulatory, Data Management, Digital, Quality, and Technology, and collaborate effectively with programmers, data scientists, ML engineers, and data engineers.
 * Communicate complex quantitative concepts and model limitations clearly to technical and nontechnical audiences; represent Alcon in scientific, regulatory, and external collaborations.
 * Mentor and develop quantitative scientists and strengthen organizational capability in statistical modeling, data science, ML, deep learning, reproducible research, and responsible AI.
 * Contribute to the long-term strategy, operating model, and scientific direction of the Statistics organization.