이 공고, 이렇게 물어볼 겁니다

이전 회사에서 MMM 또는 MTA 모델을 구축할 때, 데이터의 비선형성과 시계열적 의존성 문제를 어떻게 해결하고 모델의 정확도(Incrementality 측정)를 확보했는지 구체적인 방법론과 결과를 설명해 주세요. (꼬리질문: 특정 모델링 기법(예: Shapley Value, Hierarchical Modeling)을 선택한 이유는 무엇입니까?)

🎯 지원자가 단순한 모델 구현을 넘어, 복잡한 마케팅 데이터의 구조적 문제를 이해하고 이를 해결하기 위한 고급 방법론을 적용했는지 확인한다.

성장 마케팅 팀이 특정 채널 예산을 재분배해야 하는 상황에서, 당신이 설계한 실험 프레임워크(A/B 테스트 설계)는 기존의 전통적인 인과관계 추론 방식과 비교하여 어떤 차별점을 가지며, 실제 예산 최적화에 어떤 기여를 했는지 수치로 제시해 주세요.

🎯 실제 비즈니스 요구사항을 기술적 실험 설계로 전환하고, 이를 통해 예산 최적화라는 핵심 목표에 어떻게 기여했는지 확인한다.

당신이 주도한 데이터 사이언티스트 팀원들이 복잡한 머신러닝 모델을 실제 프로덕션 환경에 배포하고 스케일링하는 과정에서 발생한 가장 큰 기술적 장애물은 무엇이었고, 이를 해결하기 위해 어떤 기술적 아키텍처 변화를 이끌어냈는지 설명해 주세요.

🎯 개인 역량뿐만 아니라, 팀을 이끌고 기술적 난제를 해결하며 모델을 '실제 운영 환경(Production)'으로 전환하는 엔지니어링 역량을 확인한다.

마케팅 효율성 측정 시, '증분성(Incrementality)'과 '시너지/상쇄(Synergy/Cannibalization)' 개념은 데이터 해석에 매우 중요합니다. 이 두 개념을 실제로 모델에 어떻게 통합하여 측정했으며, 어떤 데이터 변수를 통해 이러한 비선형적 효과를 분리해냈는지 구체적인 사례를 들어 설명해 주세요.

🎯 지원자가 단순한 통계 계산을 넘어, 마케팅 과학의 핵심 도메인 지식을 깊이 이해하고 이를 모델에 전략적으로 반영할 수 있는지 확인한다.

데이터 과학자로서 비즈니스 부서와 기술 팀 사이에서 의견 충돌이 발생했을 때, 복잡한 마케팅 과학 방법론을 비기술적인 이해관계자들에게 어떻게 효과적으로 설명하고, 그들의 의사결정을 데이터 기반으로 이끌어냈던 경험을 공유해 주세요.

🎯 기술적 전문성을 비즈니스 언어로 번역하고, 협업을 통해 목표를 달성하는 커뮤니케이션 및 리더십 능력을 평가한다.

질문만 읽으면 컨닝이에요. 소리 내어 답해보세요 — 어디서 틀어지는지 짚어드립니다.

공고 내용

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 t

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