Senior Applied Economist, Causal Inference & Forecasting

Navan is seeking a Senior Applied Economist to join the Data Science & Machine Learning team. This is a foundational, "first-of-its-kind" role at Navan, designed for a technical leader who can bridge the gaps between hands-on machine learning, rigorous economic theory, and driving business outcomes.

In this role, you will be the primary architect of our internal economic "brain." You will move beyond point-estimate forecasting to build sophisticated models that account for market nuances, uncertainty, and causal drivers. You will partner closely with Finance, Treasury, and FP&A to steer the company's financial trajectory, while providing the strategic frameworks that Sales and Pricing teams use to maximize customer adoption and revenue.

What You'll Do:

• Next-Generation Forecasting: Uplevel our existing forecasting pipelines (currently built on Prophet). You will integrate econometric rigor to improve accuracy and, crucially, provide a range of likely outcomes (probabilistic forecasting) that Finance and Treasury can rely on for risk management.
• Causal Inference & Strategy: Design and execute experimental and quasi-experimental frameworks to identify the "levers" of the business. You will answer critical questions regarding price elasticity, product feature attribution, and the ROI of sales incentives.
• Strategic Blueprinting: Partner with Sales and Account Management to create data-driven frameworks for pricing and customer retention. You will translate complex causal models into actionable blueprints for go-to-market teams.
• Production-Level Data Science: Work hands-on within our ML infrastructure. You will write production-quality Python code to deploy models into our AWS and Snowflake-based ecosystem, ensuring your insights are automated and scalable.
• Internal Advisory: Act as the subject matter expert on economic literature and methodology, translating technical findings into strategic recommendations for executive leadership.

What We're Looking For:

• Education: An advanced degree (PhD preferred, Masters required) in Economics, Statistics, or a related quantitative field with a heavy emphasis on econometrics or causal inference.
• Experience: 4+ years of post-academic experience in an applied research, finance, or data science role, ideally within a high-growth tech environment or fintech.
  • Technical Proficiency:
    • Deep expertise in Python and its data science ecosystem (pandas, statsmodels, scikit-learn, etc.).
    • Advanced SQL skills, with experience querying large-scale data warehouses like Snowflake .
    • Experience working in production environments and a strong understanding of the ML lifecycle is nice to have.
• Econometric Mastery: Proven ability to apply advanced methods (e.g., Synthetic Control, IV, Diff-in-Diff, Structural Modeling) to messy, real-world datasets.
• Self-Starter Mentality: Experience functioning in "underdefined" spaces. As our first economist, you must be comfortable setting the roadmap.
• Communication: The ability to explain not just the "what," but the "why" and the "what if." You can communicate uncertainty and risk to a CFO just as clearly as you can discuss model architecture with an ML Engineer.
• Preferred Qualifications:
  • Prior experience in Fintech, Payments, or Travel industries.
  • Experience building and scaling "first-of-their-kind" functions within a data organization.

The posted pay range represents the anticipated low and high end of the compensation for this position and is subject to change based on business need. To determine a successful candidate's starting pay, we carefully consider a variety of factors, including primary work location, an evaluation of the candidate's skills and experience, market demands, and internal parity.

For roles with on-target-earnings (OTE), the pay range includes both base salary and target incentive compensation. Target incentive compensation for some roles may include a ramping draw period. Compensation is higher for those who exceed targets. Candidates may receive more information from the recruiter.

Pay Range

$121,500-$270,000 USD