• Direct annotation by managing annotation budgets, sourcing data, and providing statistical analysis of annotations • Maintain and expand metrics used to evaluate agent performance and fairness • Create evaluation packages for Data Scientist and Machine Learning Engineers to use when running experiments • Ensure accurate, regular reporting on evaluation metrics to stakeholders • Manage periodic re-evaluation of production agents using user data and annotation

• Evaluate the effectiveness of new advertising channels • Set up and improve monitoring of statistical anomalies in metrics • Improve long‑term payback prediction models • Generate hypotheses to improve acquisition/product feeds/ad creatives/channel split/targets, etc. • Conduct data studies to uncover insights • Write ETL pipelines (data marts, alerts, and predictions) • Ask “why” and “what problem are we solving”

• Comfortable being in a hybrid role, working between the business, other data scientists, and data engineers to reveal what trends and KPIs matter. You will primarily focus on advancing conviction in decision making throughout Juul Labs • Design robust, reusable and scalable data solutions and data mining frameworks to automate analysis. We largely use DBT, BigQuery, Hex, Vertex AI, and Tableau today – however are open to adapt • Build models to analyze various datasets across the organization, largely leveraging our vast commercial sales and execution data • Partner and collaborate with Data Engineers in active development of large-scale engineering projects across the entire lifecycle of Data from source, ingestion, modeling, and mining • Work in an agile environment using Trello • Develop new systems and tools to enable stakeholders to consume and understand data more intuitively • Create and establish design standards and assurance processes to ensure compatibility and operability of data connections, flows and storage requirements • Keep JUUL Labs on the cutting edge of data technology • Perform related duties as assigned, within your scope of practice

• Act as strategic partner for stakeholders and cross-functional collaborators to identify business opportunities and enhance business strategies with novel data science methods in the live event space • Define and execute on roadmaps for measuring the impact of content merchandising and improving member experience with Causal Inference and Machine Learning • Partner closely with other business leaders, product managers, and other data scientists to refine and scale Causal Inference model based systems • Present your research and insights to all levels of the company • Become a regional expert on Merchandising and Content Understanding Data Science and Engineering, helping educate and connect with regional offices