Note: The job is a remote job and is open to candidates in USA. iHerb is a company committed to making healthy living accessible globally through innovation in e-commerce. The Pricing Data Scientist role is a high-autonomy position responsible for owning end-to-end pricing analytics and measurement, providing actionable insights to influence senior leadership. This role requires building predictive pricing models, conducting competitive pricing analysis, and delivering clear recommendations to support pricing strategy and decisions.
Responsibilities
- Own end-to-end pricing analytics, modeling and measurement in close partnership with the Pricing organization, supporting day-to-day pricing decisions as well as longer-term strategy refinement
- Build, validate, and maintain applied pricing models (e.g., elasticity, incrementality, sensitivity tiers) that balance statistical rigor with real-world constraints and imperfect data
- Design and execute measurement approaches for pricing tests and promotions, including A/B tests and quasi-experimental methods, accounting for seasonality, halo, and cannibalization
- Lead competitive pricing analytics, including validation of external pricing data, imputation logic for incomplete coverage, and ongoing quality monitoring to ensure confidence in insights
- Translate complex analytical outputs into clear, decision-ready insights, articulating implications, tradeoffs, and recommended actions to pricing leadership and senior executives
- Partner closely with BI Analytics and Data Engineering to shape pricing datasets, contribute to data modeling where needed, and ensure analytical outputs are scalable and reusable
- Independently develop analytical workflows using SQL and Python, moving fluidly between data exploration, modeling, and insight generation without reliance on heavy guidance
- Contribute to the development of pricing dashboards and recurring analytical outputs for the Pricing team, prioritizing clarity, usability, and decision relevance over visual polish
- Continuously refine pricing measurement frameworks as the business evolves, balancing speed, accuracy, and practicality in a fast-moving global environment
- Experience deploying, monitoring, or operationalizing pricing or predictive models in a production analytics or ML environment (e.g., Databricks, scheduled pipelines, or decision-support workflows)
Skills
- Strong applied quantitative background with demonstrated experience designing, building, and deploying Python-based data science models, including production workflows, to inform pricing, promotions, or commercial decisions in a retail or eCommerce environment
- Hands-on expertise with SQL and Python, with the ability to independently extract, manipulate, model, and analyze large datasets end-to-end
- Experience designing and interpreting pricing or promotional measurement, including experimentation (A/B testing) and quasi-experimental approaches, with comfort navigating imperfect data and incomplete controls
- Practical experience with pricing concepts such as elasticity, price sensitivity, discounting, promotions, and incrementality, with an emphasis on directional insight over theoretical precision
- Proven ability to translate analytical outputs into clear, actionable insights, articulating implications, risks, and tradeoffs to senior business stakeholders
- Comfort operating with ambiguity and limited guidance, demonstrating sound judgment, prioritization, and bias toward execution in fast-moving environments
- Strong analytical problem-solving skills paired with business intuition, enabling independent ownership of complex measurement problems from framing through delivery
- Ability to collaborate effectively across Analytics, Pricing, Finance, and Engineering, balancing technical rigor with pragmatic business needs
- Typically requires ten (10) years of progressive experience in data science, analytics, or quantitative analysis roles, with a demonstrated track record of applying data-driven insights to pricing, promotions, or commercial decision-making in a retail or e-commerce environment
- Candidates should have hands-on experience building and applying analytical or data science models, working directly with large, real-world datasets, and partnering closely with business stakeholders
- Prior experience supporting pricing strategy, experimentation, or promotional measurement in fast-paced, ambiguous environments is strongly preferred
- Degree in Engineering, Math, Statistics, Finance, or Computer Science required
- Experience supporting pricing decisions in a global or multi-market retail or e-commerce environment, including regional pricing variation or localized promotions
- Familiarity with competitive pricing intelligence data, including validation, normalization, and imputation of external price sources
- Experience partnering closely with Pricing, Finance, or Commercial Strategy teams to inform margin, contribution, or profitability-focused decisions
- Hands-on experience building reusable analytical frameworks or standardized measurement templates that scale
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