Applied Scientist

Juul Labs's mission is to transition the world’s billion adult smokers away from combustible cigarettes, eliminate their use, and combat underage usage of our products. We have the opportunity to address one of the world’s most intractable challenges through a commitment to exceptional quality, research, design, and innovation. Backed by leading technology investors, we are committed to the same excellence when it comes to hiring great talent.

We are a diverse team that is united by this common purpose and we are hiring the world’s best engineers, scientists, designers, product managers, operations experts, and customer service and business professionals. If the opportunity to build your career is compelling, read on for more details.

The Applied Scientist will turn large and varied commercial datasets into actionable items for leadership. We model direct and the syndicated views of the market (Circana, NielsenIQ, IRI, Skupos, Numerator, and store-level scan data), we measure whether our commercial programs actually effect change, and we give the commercial, finance, and executive teams a clear read on our fast-moving, hyper-competitive category. The Applied Scientist team is a small group but creates impactful changes at Juul. The successful candidate will have the ability to support leadership on pricing, distribution, and investment decisions that are made on a regular basis. The team is small and high-leverage, and our work shapes pricing, distribution, and investment decisions on a regular basis.

We are looking for someone who feels equally at home building a clean, well-tested data model over billions of rows of transaction data as they do designing the analysis that tells us whether a promotion drove incremental sales or simply rewarded customers who would have bought anyway. We believe the best data people do both, and we have built the team around that conviction.

• →Partner directly with commercial, finance, and executive stakeholders to proactively transform vague, complex business questions into scoped, actionable analytical problems, anticipating organizational needs before they are explicitly asked
• →Design and run rigorous experimental and quasi-experimental analyses (e.g., Diff-in-Diff, propensity methods) to evaluate promotions, measure causal impact, and model category economics like price elasticity and regulatory tax impacts
• →Architect and maintain large, complex commercial datasets using SQL and dbt on BigQuery, and build, deploy, and monitor robust market-share and demand forecasting models to drive seven-figure decisions
• →Build the predictive models and performance metrics that guide field operations, directly determining where and how field sales managers allocate their time to maximize store-level value
• →Deploy LLMs and AI agents to classify unstructured commercial data (e.g., receipts, transactions) and build internal tools that democratize data access and enable stakeholders to answer their own questions.

• SQL expertise, with the judgment to write models that are correct, efficient, and maintainable
• A strong analytical and statistical foundation.
• Experience with experimental design, causal inference, and the instinct to tell a real result from an artifact of how the data was selected
• Working fluency in Python for analysis (pandas and the surrounding ecosystem)
• Ability to connect data to commercial reality, and effectively communicate with key stakeholders about your findings.
• Fluent in using AI tools to multiply your own output and to build tools for others.
• 5 years of experience building analysis and models in industry
• Preferred experience with commercial, retail, CPG, or syndicated market data (Circana, NielsenIQ, IRI, POS or scan data).
• Preferred ability to view analytics engineering craft: version control, testing, documentation, and codebase hygiene.
• Preferred familiarity with Juul’s stack and the broader modern data ecosystem.

• Bachelor's degree required
• Preferred Master’s degree in a quantitative field (statistics, economics, math, computer science, or similar)