Staff, Data Scientist (Growth Analytics)

• Build machine learning models, perform proof-of-concept, experiment, optimize and deploy your models into production.

• Improve the analytical skillsets by adopting AI driven solutions.

• Design and implement causal inference methodologies (e.g., RCTs, DiD, RDD, IV methods) to measure true incremental impact of marketing initiatives and product changes

• Develop and deploy causal models that account for selection bias, confounding factors, and treatment heterogeneity in production environments

• Establish scalable, efficient, automated processes for causal inference analysis, including experimental design, power calculations, and sensitivity analyses

• Work with cross-functional teams to identify business opportunities where causal questions are critical, translating business problems into causal identification strategies

• Analyze large-scale structured and unstructured data to isolate causal effects from correlational patterns; develop deep-dive analyses to drive customer engagement and retention

• Design rigorous experimental and quasi-experimental frameworks to test product ideas and marketing strategies with clear identification of causal parameters

• Create robust counterfactual analyses and develop methodologies to estimate treatment effects in observational settings

• Communicate findings to senior leaders, distinguishing between correlation and causation, and evangelize evidence-based business decisions

• Master's degree in a quantitative field - Economics, Statistics, Computer Science, Mathematics, Engineering or related fields

• Strong programming skills in Python or R with experiences including causal inference libraries (e.g., DoWhy, CausalImpact, EconML, CausalML)

• Advanced SQL skills for complex data manipulation; experience researching and manipulating large datasets

• 8+ years working experience designing and analyzing experiments, natural experiments, observational studies and causal inference methodologies in industry settings

• 8+ years working experience in the engineering teams that build large-scale ML-driven user-facing products

• Experience with state of the art ML modeling techniques and approaches

• Hands-on experience training and applying models at scale.

• Demonstrated expertise in causal identification strategies, potential outcomes framework, and graphical causal models

• Experience with propensity score methods, instrumental variables, difference-in-differences, synthetic controls, and other causal techniques

• Ability to effectively communicate complex causal concepts and findings to technical and non-technical stakeholders

• Self-starter who takes initiative to identify and address potential sources of bias and confounding

• Outstanding team player with a rigorous scientific mindset focused on identifying true causal effects

• Hands-on experience adopting LLM models to analytical space, with agent building and adoption.

• Experience with manipulating massive-scale customer and clickstream data for causal analysis

• Deep expertise in causal machine learning methods (meta-learners, causal forests, orthogonal ML)

• Experience with heterogeneous treatment effect estimation and personalized interventions

• Experience designing and analyzing geo-experiments, switchback tests, and other complex experimental designs

• Knowledge of Bayesian approaches to causal inference and uncertainty quantification

• Excellent communication skills, with ability to explain causal identification strategies and limitations to different audiences, including executives

• Experience implementing causal inference methods in production systems for real-time decision making

• Publication record or contributions to the field of causal inference

• Experience in working on, backend and ML systems for large-scale user-facing products, and have a good understanding of how they all work.

• Application Review - Phone Interview - Onsite (or Virtual Onsite) Interview - Offer

• The exact nature of the recruitment process may vary according to the specific job and may be changed due to scheduling or other circumstances.

• Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage.

• This job posting may be closed prior to the stated end date for application if all openings are filled.

• Coupang has the right to rescind an offer of employment if a candidate is found to have submitted false information as part of the application process.

• Those eligible for employment protection (recipients of veteran's benefits, the disabled, etc.) may receive preferential treatment for employment in accordance with applicable laws.

• Hiring may be restricted in case the legal qualifications required for hiring and work performance is not met.

• This is a full-time regular position and includes 12 weeks of probation period; provided, however, the probationary period may be either skipped, shortened or extended if necessary for business purposes.

• Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice is located below.

• A job applicant, who has applied but not been finally selected for a position at Coupang (the “Company”), may request the Company to return his/her hiring documents submitted pursuant to the Fair Hiring Procedure Act. However, this will not apply where the hiring documents were submitted via the website of the Company or e-mail, or where the job applicant submitted those documents voluntarily without a request from the Company. In addition, if the hiring documents were destroyed due to a natural disaster or any other reasons not attributable to the Company, such documents will be deemed to have been returned to the job applicant.
• A job applicant who wishes to request the return of his/her hiring documents pursuant to the main sentence of paragraph 2 above should fill out a “Request for Return of Hiring Documents” [Annex Form No. 3 in the Enforcement Rule of the Fair Hiring Procedure Act] and submit It by email (

• The above paragraphs 1 - 4 shall only apply when the labor-related laws of Korea govern the application. They are otherwise not applicable.

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