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Responsibilities
Own end‑to‑end pricing analytics, modeling and measurement in 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, trade‑offs, 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 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)
Knowledge, Skills and Abilities Required
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 trade‑offs 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
Preferred
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
Experience Requirements
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.
Education Requirements Degree in Engineering, Math, Statistics, Finance, or Computer Science required. Advanced degrees are welcome but not required.
Equal Opportunity Employer iHerb is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status. iHerb provides equal employment opportunities to all applicants for employment and prohibits discrimination and harassment.
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Languages
- English
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