Staff, Data Scientist (Ads Analytics)

Coupang•Mountain View, CA

About The Position

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’re collectively disrupting the multi-billion-dollar e-commerce industry from the ground up. We are one of the fastest-growing e-commerce companies that established an unparalleled reputation for being a dominant 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 since our inception. We are all entrepreneurs 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 Coupang Ads is the advertising platform team within Coupang. We are a globally distributed team focused on delivering advertising solutions for those that sell products on our platform. This includes display, search, performance, brand and video ads offerings. The Coupang Ads Product Analytics team maximizes revenue from Coupang advertising solutions and helps our user get value from ads. We are responsible for helping drive product strategy across entire ads business. We bring data together to generate actionable insights, answer complex business questions, and help us deepen our understanding of Customers. Our focus is on Coupang’s ads business on Ecommerce, and we do this by analyzing KPI’s, analyzing the success of A/B and multivariate testing initiatives.

Requirements

Master’s Degree in Computer Science, Electrical Engineering, Math or a closely related field
4 years of Data Scientist experience in customer-facing technology companies
Expertise in experimentation and statistics

Nice To Haves

The ideal candidate has 6+ years of experience as a data scientist performing ads data analysis, ML model building and optimization, financial analysis, market research, and executive reporting.
Previous experience in AdTech / Advertising (search, display, branding, retargeting, social, streaming video…), Digital Marketing, Ad Agency, or related domains.
Strong SQL
Python or R for statistical analysis
Hive, Presto, Airflow
Experience with ML modeling design, deployment, and optimization
Strong statistics background
Experience in working on backend and ML systems for large-scale user-facing products
Data visualization (e.g., Tableau, Qlik, Looker, Power BI)
Excellent quantitative analysis skills
Strong presentation and communication skills
Highly organized and detail-oriented
Ability to manage multiple projects simultaneously for multiple stakeholders in a fast-paced organization

Responsibilities

Product and Monetization strategy: develop and test various auction and monetization models. Provide simulations of the possible impact of changes to be introduced on revenue
Metrics: deliver a set of Key Performance Indicators, used for reporting on product adoption, usage, performance, and churn. Provide visibility into the effectiveness of the group’s monetization efforts
Forecasting: design and maintain inventory and revenue forecasting models as a critical component of the revenue optimization process. Assist with analyzing sales patterns to identify trends and aid financial forecasting
A/B Testing: design, conduct, analyze and uncover insights from A/B tests to inform and prioritize product iterations. Do this with statistical rigor and perform cohort studies to answer “why?” on test result drivers
Tell a Concise Story: At completion of Tests, build timely, compelling, fact-based test recaps that strongly communicate the business ‘story’ behind the data and test outcomes.