Director, Statistical Innovation and Data Science

At Alcon, we're passionate about enhancing sight and helping people see brilliantly. With more than 25,000 associates, we innovate fearlessly, champion progress, and act swiftly to impact global eye health. We foster an inclusive culture, recognizing your contributions and offering opportunities to grow your career like never before. Together, we make a difference in the lives of our patients and customers.

Are you ready to join us?

As Director, Statistical Innovation and Data Science, you will provide strategic and scientific leadership at the intersection of statistics, statistical modeling, data science, machine learning (ML), deep learning, and AI for evidence generation. You will be a quantitative scientist who combines rigorous statistical thinking with modern computational methods to solve complex scientific problems and translate innovation into reproducible, validated, and scalable capabilities for a regulated environment.

This role is on-site in our Fort Worth, Texas (or remote in the US) location and a typical day would include:

Provide senior quantitative leadership for clinical development and evidence generation, applying sound statistical principles to study design, analysis, interpretation, and development decisions.

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.

Preferred Qualifications 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,…