Ann Arbor, Michigan, United States
I am a data science and AI leader focused on helping organizations scale AI with confidence. My work centers on translating business priorities into practical technical solutions, building alignment across diverse teams, and creating the conditions for responsible, effective adoption of emerging technologies. I bring a strong technical foundation in data science and statistics, with experience applying those disciplines to AI enablement, experimentation, and practical decision-making. I enjoy partnering with technical teams, business leaders, and governance stakeholders to clarify objectives, shape solution design, and ensure that AI-driven work is not only innovative, but useful, measurable, and trusted. I am particularly motivated by opportunities to connect strategy with execution, turning ideas into operating models, frameworks, and solutions that help teams move faster while maintaining the discipline needed to manage risk, build trust, and deliver sustainable business value.
-> Supervise a team of AI Engineers in developing AI-driven solutions for clinical operations, supporting initiatives across trial efficiency and workflow automation -> Prototype agentic AI frameworks to enable intelligent, multi-agent systems that assist in clinical task coordination, trial monitoring, and real-time data interpretation -> Lead a company-wide forum that empowers employees across diverse functions to integrate AI into daily workflows, fostering cross-disciplinary understanding and responsible adoption of emerging technologies -> Evaluate and integrate new internal and external data to refine predictive models for clinical trial milestone forecasting and dynamic reforecasting
-> Championed collaboration between data science, engineering, and product teams, enhancing experimentation workflows and promoting a cohesive approach to hypothesis-driven innovation -> Bridged business goals and data science initiatives by designing focused experiments that directly supported strategic priorities, ensuring decisions are backed by robust, data-driven insights -> Led the rollout of the Content Rules Experimentation Module within Charter’s A/B testing platform, accelerating the testing and optimization of personalized content strategies to improve key performance indicators -> Cultivated a data-first mindset across the team, mentoring junior data scientists in experimental design, statistical evaluation, and results interpretation
-> Directed 3 year-long experimentation campaigns for Charter’s digital services and customer experience teams, employing A/B testing to refine product offerings and elevate machine learning-driven content personalization -> Developed the Experimentation-Machine Learning Lifecycle framework, setting organizational standards for the development, validation, and deployment of machine learning applications in real-world scenarios -> Delivered high-level data insights to company leadership, transforming complex analytical findings into strategic recommendations that shaped feature development and informed future experimentation plans -> Conducted certification trainings for product stakeholders to utilize Charter’s in-house A/B testing platform, focusing on the foundations of experimentation, hypothesis generation, metric selection, and data-driven decision frameworks
-> Supervised the quantitative team within the Denver-Seattle Center of Innovation (COIN), driving innovation and excellence in data analytics to support organizational objectives -> Oversaw the strategic planning and resource allocation for 24 multi-year, multi-site studies, demonstrating the ability to manage complex analytics initiatives from inception to completion -> Cultivated collaborative partnerships with stakeholders and academic leaders to enhance the decision-making processes for clinical programs and interventions, driving measurable improvements in effectiveness and outcomes
-> Conducted statistical analyses and economic evaluations applying methods in causal inference, clustered data, longitudinal analysis, and survival analysis -> Led comprehensive statistical consultations for principal investigators, crafting design plans for upcoming studies to ensure robust and methodologically-sound research initiatives -> Produced insightful analytic summaries, statistical results, and data visualizations for manuscripts and reports, contributing to the effective communication of research findings -> Engineered analytic data sets by pulling, merging, and cleaning relational data, demonstrating precision and attention to detail in preparing the groundwork for advanced data analysis