
Buy, sell, and discover.
Applied Researcher 2 - Search Ads
At e Bay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.
Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet.
Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.
About the team and the role:
The Search Ads Team is the biggest contributor to e Bay’s advertising program. We innovate at the heart of e-commerce search and advertising, with the ambitious goal of redefining e-commerce advertising. We craft optimized experiences for buyers and sellers on e Bay. We innovate rapidly in this space and there is no shortage of new challenges for motivated individuals.
We are looking for stellar applied researchers to join us and build the next generation of online advertising products in e Bay search. If you enjoy the scale and technical complexity of advertising and want to be at the frontier of applied research in advertising in e-commerce, join now. Help us redefine advertising at e Bay.
What you will accomplish:
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Seek scientifically valid solutions that deliver real value to e Bay customers
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Build machine learning models and data pipelines to deliver insightful yet practical solutions
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Work with multiple teams to help promote standard scientific methodologies and processes in your field
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Present key technical and novel research work in public forums and conferences
What you will bring:
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MS or PhD in Computer Science, Statistics, Mathematics, or equivalent
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0-1 years (with PhD) or 1-3 years (with MS) of industrial experience in a related field
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Industrial experience with one or more of the following: classification, regression, recommendation systems, targeting systems, ranking systems, fraud detection, online advertising, or related
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Experience in big data processing, e.g. Hadoop, SQL, Spark
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Experience with Python or R, and Java or Scala or C/C++
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2 or more related publications in quality conferences or journals
Additional Details
eBay is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, veteran status, and disability, or other legally protected status. If you have a need that requires accommodation, please contact us at talent@ebay.com. We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBay's commitment to ensuring digital accessibility for people with disabilities.
We use cookies to enhance your experience and may use AI tools for administrative tasks in the hiring process. To learn how we handle your personal data and use AI responsibly, please visit our Talent Privacy Notice, Privacy Center, and AI Hiring Guidelines.
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eBayについて

eBay
PubliceBay Inc. is an American multinational e-commerce company based in San Jose, California, that allows users to buy or view items via retail sales through online marketplaces and websites in 190 markets worldwide.
10,001+
従業員数
San Jose
本社所在地
$28.1B
企業価値
レビュー
10件のレビュー
3.8
10件のレビュー
ワークライフバランス
3.2
報酬
2.8
企業文化
4.1
キャリア
3.0
経営陣
2.7
72%
知人への推奨率
良い点
Supportive team culture and colleagues
Good benefits and health coverage
Flexible work arrangements
改善点
Management issues and lack of direction
Limited career advancement opportunities
Compensation below expectations
給与レンジ
2,735件のデータ
Junior/L3
Mid/L4
Senior/L5
Junior/L3 · Data Science Analyst 2
1件のレポート
$174,200
年収総額
基本給
$134,000
ストック
-
ボーナス
-
$174,200
$174,200
面接レビュー
レビュー4件
難易度
3.0
/ 5
期間
14-28週間
体験
ポジティブ 0%
普通 75%
ネガティブ 25%
面接プロセス
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Interview
5
Team Matching
6
Offer
よくある質問
Coding/Algorithm
Technical Knowledge
Behavioral/STAR
Past Experience
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