• Design, build, and optimize data pipelines for reach, frequency, and audience measurement using PySpark and Snowflake • Develop and implement clean room measurement methodologies for privacy compliant audience analysis • Analyze media and audience datasets to identify trends, anomalies, and optimization opportunities • Collaborate with product, engineers and data scientists to deliver scalable solutions for advertising analytics • Communicate findings and recommendations to technical and nontechnical stakeholders • Contribute to documentation, best practices, and process improvements

• Collaborate with multidisciplinary teams, communicating effectively to achieve shared objectives. • Work on solving complex problems, developing creative and innovative solutions. • Use critical thinking to analyze data, identify patterns, and make evidence-based decisions. • Pursue continuous learning, adapting to new technologies and trends to enhance your skills and contribute to project success.

• Lead project teams of world-class data scientists developing and applying robust, generalizable solutions to core scientific problems. • Work with a diverse set of stakeholders to develop project plans that support program needs, develop alignment between functional teams and stakeholders, and prioritize. • Work with an array of data types alongside genetic sequencing data, including electronic medical records, multi-omics data, and other data modalities using R or Python in cloud environments. • Be comfortable with scientific uncertainty and embrace curiosity and creative solutions. • Collaborate with drug discovery and clinical development teams to help ensure the relevance and impact of the insights generated by you and your teammates. • Be a dynamic and active team member, championing and adopting shared coding standards, participating in code review, and providing regular updates of your work and input into the work of your colleagues

• Design, build, and productionize machine learning models focused on: • Knowledge extraction from unstructured data (e.g., NER, entity linking) • Graph-based learning and inference • Entity resolution and relationship discovery • Evaluate and leverage existing ML models and frameworks to solve real-world problems efficiently • Partner closely with backend and graph engineers to integrate ML models into production services and APIs • Contribute to the design and evolution of knowledge graphs and ontologies • Perform exploratory data analysis (EDA) to inform modeling decisions and system design • Own ML components end-to-end, including experimentation, evaluation, deployment, and iteration • Help shape best practices for applied ML within the Knowledge Layer team