• Design and implement scalable data pipelines to ingest, process, and transform large datasets (structured & unstructured). • Develop, validate, and optimize supervised and unsupervised machine learning models leveraging Python, SQL, and modern libraries. • Conduct feature engineering, model selection, and statistical modeling to deliver high-impact solutions. • Build and expose model APIs or containerized workflows for seamless integration and deployment in production environments. • Apply MLOps best practices to model versioning, testing, monitoring, and deployment. • Work with Big Data technologies such as Databricks and Snowflake to unlock analytics at scale. • Orchestrate complex workflows using tools like Airflow or Dagster for automation and reliability. • Collaborate with AI teams to refine prompt engineering and leverage AI tooling for model fine-tuning and augmentation. • Maintain familiarity with leading cloud platforms (AWS, Azure, GCP) for model training, deployment, and infrastructure management. • Partner with product, engineering, and business teams to translate requirements into technical solutions.

• 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