• Design ranking and retrieval models for main page, related content, and search • Build real-time recommendation systems for videos, shorts and infinite scroll timelines • Develop end-to-end recommendation pipelines, contributing to backend • Explore algorithms for Shorts generation

• Experience daily immersion in automotive industry data • Collaborate with data science and engineering teams to build and maintain model training and forecasting/prediction ETL pipelines with appropriate data mappings, anomaly detection, feature engineering, and data imputation strategies • Deploy models and ETL pipelines in a reusable framework to generate predictions or forecasts for use in existing and net-new products • Lead the creation of model and data drift monitoring pipelines and reporting • Communications of technical topics to both lay and technical business stakeholders, product, and senior data science leadership while requiring minimal feedback for communication documents and presentations • Leads quarterly planning for major ML deployment projects with minimal supervision

• Leading ML research, experimentation, and development from concept to deployment • Designing and implementing novel ML/AI approaches, staying current with cutting-edge research and methodologies • Architecting ML pipelines, training infrastructure, and model evaluation frameworks • Collaborating with cross-functional teams: Working closely with product managers, and other stakeholders to ensure the AI/ML solutions align with the organization's goals and objectives • Communicating effectively: Clearly document your work and effectively communicate your findings, insights, and recommendations to both technical and non-technical stakeholders, fostering a collaborative and informed environment • Guiding ML engineering decisions including model selection, feature engineering, and optimization strategies • Translating academic insights into practical applications • Mentoring junior ML engineers and establish ML best practices across the team • Working closely with AI Systems Tech Leads to ensure delivery of optimized AI systems and services to production • Enforcing data governance and privacy: Follow processes and implement methods that ensure data governance, privacy, and security

• Lead the strategy, design, and implementation of AI and machine learning solutions to support R&D digital transformation and operational efficiency. • Drive the adoption of AI across R&D by identifying high-impact use cases, building business cases, and partnering with scientific, statistical, clinical, and technology stakeholders. • Develop and deploy AI/ML models, tools, and platforms that improve the efficiency of digitalization systems, including clinical trial design, clinical data workflows, statistical analysis processes, document generation, knowledge management, and decision-support systems. • Evaluate emerging AI technologies, including generative AI, large language models, intelligent agents, automation frameworks, and advanced machine learning approaches, and assess their applicability in regulated pharmaceutical R&D environments.