Director, Statistical Innovation and Data Science

Alcon Research•Fort Worth, TX

About The Position

At Alcon, the Director, Statistical Innovation and Data Science provides strategic and scientific leadership at the intersection of statistics, statistical modeling, data science, machine learning, deep learning, and AI for evidence generation. The role 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. The Director provides senior quantitative leadership for clinical development and evidence generation, applying statistical principles to study design, analysis, interpretation, and development decisions. The role develops and applies advanced statistical models; leads data science and machine-learning applications; evaluates deep-learning approaches, including for ophthalmic imaging and multimodal data; and establishes rigorous model-development and validation practices. The Director also evaluates emerging approaches such as generative AI and translates useful prototypes into reusable analytical capabilities with appropriate human oversight and governance. The position partners across Statistics, Clinical Development, Regulatory, Data Management, Digital, Quality, and Technology, and collaborates with programmers, data scientists, ML engineers, and data engineers. It communicates quantitative concepts and model limitations to technical and nontechnical audiences, represents Alcon in scientific, regulatory, and external collaborations, mentors quantitative scientists, and contributes to the long-term strategy and scientific direction of the Statistics organization. The role is on-site in Fort Worth, Texas, or remote in the US, with travel up to 10%. Relocation is available and sponsorship is offered.

Requirements

Bachelor’s degree or equivalent years of directly related experience; stated equivalencies include high school plus 18 years, associate degree plus 14 years, M.S. plus 7 years, or PhD plus 6 years.
10 years of relevant experience.
6 years of demonstrated leadership.
Fluency in reading, writing, understanding, and communicating in English.

Nice To Haves

Advanced degree (MSc or PhD) in Statistics or Biostatistics; a PhD in a closely related quantitative discipline 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.

Responsibilities

Provide senior quantitative leadership for clinical development and evidence generation, including 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 across clinical, imaging, digital, patient-reported, and real-world data.
Lead data science and machine-learning applications 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.
Select and compare classical statistical, machine-learning, and deep-learning approaches according to scientific objectives, assumptions, uncertainty, interpretability, validation, generalizability, and intended use.
Establish model-development and validation practices covering training, tuning, calibration, robustness, reproducibility, bias, interpretability, uncertainty, performance monitoring, and 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 with programmers, data scientists, ML engineers, and data engineers.
Communicate quantitative concepts and model limitations to technical and nontechnical audiences, and represent Alcon in scientific, regulatory, and external collaborations.
Mentor and develop quantitative scientists and strengthen organizational capabilities 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.