Senior Staff, Data Scientist (Incrementality and Attribution)

Company Introduction

We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did we ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we are collectively disrupting the multi-billion-dollar commerce industry from the ground up and establishing an unparalleled reputation for being leading and reliable force in South Korean commerce. 

We are proud to have the best of both worlds — a startup culture with the resources of a large global public company. This fuels us to continue our growth and launch new services at the speed we have been at since our inception. We are all entrepreneurial surrounded by opportunities to drive new initiatives and innovations. At our core, we are bold and ambitious people that like to get our hands dirty and make a hands-on impact. At Coupang, you will see yourself, your colleagues, your team, and the company grow every day. 

Our mission to build the future of commerce is real. We push the boundaries of what’s possible to solve problems and break traditional tradeoffs. Join Coupang now to create an epic experience in this always-on, high-tech, and hyper-connected world.  

Role Overview

The Incrementality and Attribution Team – Data Analytics and Science team is responsible for designing marketing experiments to measure incrementality, creating marketing science models to evaluate marketing efficiency, optimize budget allocation and in-channel investment optimization. 

We are looking for a Senior Staff, Data Scientist(Incrementality and Attribution) to serve as the principal technical leader for our incrementality and attribution initiatives. In this individual contributor (IC) role, you will define the high-level research direction and technical strategy to evaluate marketing efficiency and optimize budget allocation. You will be highly hands-on, architecting advanced machine learning models and designing robust incrementality testing frameworks. You will collaborate closely with our existing team of data scientists and analysts to solve complex technical challenges, and partner with business teams to translate growth marketing requirements into scalable technical solutions. 

Key Responsibilities

• Lead Technical Strategy: Act as the senior technical lead to establish the high-level research direction, methodology, and framework for incrementality testing and measurement

• Hands-on Modeling: Architect, build, and refine advanced Marketing Mix Models (MMM) and Multi-Touch Attribution (MTA) models from scratch

• Enable the Team: Guide and upskill current data scientists and analysts, helping them overcome technical hurdles and empowering them to execute, scale, and productionize models 

• Drive Business Impact: Enable the growth marketing team to make data-driven decisions and optimize budget allocation across various channels

• Bridge Tech and Business: Partner closely with cross-functional business stakeholders to translate complex business requirements into robust, production-grade technical solutions. 

• Foster Excellence: Establish best practices for marketing science methodology, code quality, and experimental rigor across the team

Basic Qualifications 

• Master’s or PhD degree in Statistics, Econometrics, Data Science, Computer Science, Operations Research, or other highly quantitative technical fields

• 10+ years of industry experience in data science, advanced machine learning modeling, or a highly quantitative marketing science role

• Deep Domain Expertise: Deep, foundational understanding of incrementality & marginality concepts, synergy & cannibalization, A/B testing design, and attribution methodology

• Proven Model Building: Demonstrated track record of hands-on experience building and deploying advanced MMM (Marketing Mix Modeling) and MTA (Multi-Touch Attribution) models at scale in previous companies

• Strong Machine Learning and Analytics Foundations: Advanced proficiency in Python and SQL, and deep expertise in statistical modeling, causal inference, and machine learning algorithms

• Technical Leadership: Proven experience acting as a tech lead, setting research directions, and unblocking team members without direct people management responsibilities

• Cross-Functional Communication: Excellent communication skills with a proven ability to translate highly technical data concepts into actionable business strategies for growth marketing and executive teams

Preferred Qualifications

• Strong background in causal inference methodologies (e.g., uplift modeling, double machine learning, synthetic controls, geo experiments, non-compliance, confounding and bias)  

• Familiar with using AI in daily work 

• Experience optimizing large-scale marketing budgets across diverse channels (paid media, affiliates, performance marketing) 

• Ability to handle multiple competing priorities in a fast-paced environment and lead the delivery of complex measurement frameworks

• Strong intuition for trade-offs between model complexity, interpretability, and business usability 

Recruitment Process and Others

• Recruitment Process 

1. Application Review - Phone Interview - Onsite (or Virtual Onsite) Interview – Offer 
2. The exact nature of the recruitment process may vary according to the specific job and may be changed due to scheduling or other circumstances. 
3. Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage. 

• Things to Consider 

1. This job posting may be closed prior to the stated end date for application if all openings are filled.    
2. 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.    
3. Coupang does not discriminate against disabled applicants or those with veteran status. We are proud to offer equal opportunities for all applicants. 

• Privacy Notice 

1. Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice located HERE. 

• Document Return Policy 

1. This notification is given pursuant to Article 11 (6) of the Fair Hiring Procedure Act.  
2. 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. 
3. 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 the request to the Company by email (recruitingops@coupang.com) In such case, within fourteen (14) days from the date of identifying the receipt of the request, the Company will send the hiring documents to the job applicant’s designated address via registered mail.  Please be informed that the job applicant is required to pay the postage on the registered mail. 
4. In preparation for a job applicant’s request for the return of hiring documents pursuant to the main sentence of paragraph 2 above, the Company shall retain the original hiring documents submitted by the job applicant for 180 days from the completion of the recruiting process.  If no request is made until the end of this period, all of his/her hiring documents will be destroyed immediately in accordance with the Personal Information Protection Act. 
5. The above paragraphs 1 - 4 shall only apply when the labor-related laws of Korea govern the application. They are otherwise not applicable.