This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Applied Scientist based in the United States.
This role sits at the intersection of advanced analytics, causal inference, and commercial decision-making, transforming massive and complex datasets into clear, actionable insights that directly shape business strategy. You will work in a highly cross-functional environment, partnering with commercial, finance, and executive stakeholders to answer high-impact questions around pricing, distribution, and investment decisions. The work spans experimental design, predictive modeling, and large-scale data engineering across billions of transaction records. You will also help evaluate the true impact of commercial initiatives, ensuring leadership can distinguish signal from noise in fast-moving markets. In addition, you will contribute to building scalable data systems and analytical frameworks that power decision-making across the organization. This is a high-ownership, high-impact role where your work directly influences strategy in a competitive, data-intensive industry.
Accountabilities:
This role is responsible for translating complex commercial questions into rigorous analytical solutions, leveraging large-scale datasets, statistical modeling, and machine learning techniques to inform strategic decision-making across the business.
- Partner with commercial, finance, and executive teams to define and solve high-impact business problems through structured analytical approaches
- Design and execute causal inference studies, experiments, and quasi-experimental analyses (e.g., Diff-in-Diff, propensity modeling) to measure impact of pricing, promotions, and investments
- Build, maintain, and optimize large-scale data models and pipelines using SQL and modern data tooling (e.g., dbt, BigQuery)
- Develop predictive models for demand forecasting, market share, and commercial performance to support strategic planning
- Apply advanced analytics to evaluate pricing elasticity, promotional effectiveness, and category economics
- Leverage LLMs and AI systems to structure unstructured commercial data and build internal tools that improve data accessibility
- Collaborate with cross-functional stakeholders to ensure insights are translated into actionable business decisions
- Establish scalable, well-documented analytical frameworks and best practices for experimentation and modeling
Requirements:
This role requires a strong applied scientist with deep expertise in statistics, causal inference, and large-scale data analysis, combined with the ability to translate technical findings into business impact.
- 5+ years of experience in applied data science, analytics engineering, or quantitative modeling roles in industry
- Strong proficiency in SQL with experience working on large-scale datasets and production-grade data models
- Solid foundation in statistics, experimental design, and causal inference methodologies
- Strong programming skills in Python (pandas and related data science ecosystem)
- Experience building and deploying predictive models and analytical frameworks in production environments
- Ability to connect analytical insights to real-world commercial decisions and communicate findings clearly to stakeholders
- Hands-on experience with LLMs or AI tools for data classification, automation, or workflow enhancement
- Preferred experience in retail, CPG, fintech, or syndicated market data environments (e.g., Circana, NielsenIQ, IRI, POS data)
- Strong understanding of analytics engineering practices such as version control, testing, and documentation
- Advanced degree in a quantitative field (statistics, economics, computer science, mathematics) preferred
Benefits:
- Competitive base salary ranging from approximately $126,000 to $206,000 depending on location and experience
- Annual performance-based bonuses and equity participation
- Comprehensive medical, dental, vision, disability, and life insurance coverage
- 401(k) retirement plan with company match
- Paid time off and company holidays
- Family support benefits including parental leave and wellness programs
- Cell phone subsidy and commuter benefits
- Learning and development opportunities and professional growth support
- Employee assistance and mental health support programs
- Remote-friendly work environment with flexibility
- Additional perks and employee discount programs.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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