• Design, implement, and optimize core bidding algorithms and auction mechanisms • Architect and scale bid landscape forecasting capabilities • Analyze large-scale marketplace dynamics to uncover deep insights and deliver algorithmic improvements • Drive offline evaluations and online A/B experiments to validate model performance and deliver measurable business impact • Collaborate cross-functionally with product, infrastructure, and engineering teams

• Design, implement, and optimize core bidding algorithms and auction mechanisms • Architect and scale bid landscape forecasting capabilities • Analyze large-scale marketplace dynamics to uncover deep insights and deliver algorithmic improvements • Drive offline evaluations and online A/B experiments to validate model performance and deliver measurable business impact • Collaborate cross-functionally with product, infrastructure, and engineering teams

• Own the production recommendation infrastructure: maintain and improve the systems that serve personalized content to millions of users, ensuring reliability, low latency, and scalability as the catalog and user base grow. • Research and prototype advanced recommendation algorithms: explore newer approaches - deep learning-based models, contextual bandits, session-based recommendations, graph-based methods - evaluate their potential, and run controlled experiments to validate uplift before production. • Produce ML models and pipelines: take prototypes (from yourself or from the team's Data Scientist) and turn them into production-grade, monitored, maintainable features integrated into the live recommendation engine. • Design scalable infrastructure: anticipate bottlenecks and design systems that can handle larger catalogs, more complex segmentations, and higher traffic - including serving layer optimization, caching strategies, and pipeline orchestration. • Build and maintain data pipelines in DBT and Databricks, ensuring clean transformations, data quality, and robust experimentation frameworks that the team can rely on. • Monitor model health in production: define retraining strategies, detect drift, and ensure recommendation quality is measured and maintained over time. • Collaborate closely with the Data Scientist and Senior Analyst to translate statistical insights and business requirements into engineering decisions.

• Design, develop, and deploy AI/ML solutions to modernize core pharmacy platforms, with a focus on scalability, reliability, performance, and security • Leverage Generative AI, LLMs, and agentic AI frameworks to automate and enhance pharmacy workflows and decision-making processes • Collaborate with business and technical stakeholders to understand pharmacy domain requirements and translate them into robust AI/ML-driven technical solutions • Contribute to architecture and technical design of AI/ML pipelines, including model selection, data integration, and deployment patterns • Establish and maintain AI/ML engineering standards, best practices, and quality assurance processes • Conduct code reviews, provide constructive feedback, and mentor engineers on modern AI/ML engineering practices • Partner with cross-functional teams including data scientists, product managers, platform engineers, and pharmacy domain experts • Translate legacy system modernization needs into scalable AI applications that enhance products, workflows, and operational efficiency • Monitor and optimize AI/ML model performance, resource utilization, and platform reliability • Collaborate with DevOps and infrastructure teams to ensure secure, scalable deployment and operations of AI/ML solutions • Stay current with advancements in AI/ML frameworks, Generative AI, LLMs, and pharmacy technology modernization