トレンド企業

Netflix
Netflix

See What's Next

Data Scientist 5 - Ads Experimentation

職種データサイエンス
経験ミドル級
勤務地USA - Remote
勤務リモート
雇用正社員
掲載3週間前
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At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.

The Ads Data Science & Engineering team is responsible for the foundational logic of the Netflix ads business. We develop the analyses, tools, and predictive algorithms that drive member joy and advertiser value. As a Data Scientist in this 0–1 space, you will not just execute tasks; you will be a primary architect of our experimentation roadmap, helping us navigate the transition from a nascent offering to a sophisticated global marketplace.

Key Responsibilities

  • Scalable Experimentation & Frameworks: Design and execute rigorous experimental frameworks. You will lead the transition from manual analysis to automated, scalable solutions integrated with the Netflix Experimentation Platform, defining best practices that ensure trustworthy decision-making at scale.

  • Marketplace Dynamics & Auction Theory: Drive the advancement and implementation of biddable media models and dynamically priced auctions. You will develop strategies to optimize marketplace mechanics, ensuring balance between supply, demand, and member experience.

  • Global Expansion & Scaling: Partner with Product and Engineering to solve "cold-start" measurement challenges and rapidly scale our ad platform across diverse international markets.

  • Strategic Thought Leadership: Act as a high-level consultant to Product, Strategy, and Engineering teams. You will autonomously identify research opportunities, quantify their potential business impact, and advocate for resource allocation to pursue them.

  • Collaborative Partner: Cultivate strong partnerships with cross-functional stakeholders including product, engineering, operations, design, and consumer research.

  • Technical Excellence: Deliver end-to-end solutions using advanced causal inference, machine learning, and data exploration, maintaining a high bar for documentation and reproducibility.

Requirements

  • Advanced Quantitative Background: MS or PhD in a quantitative field (e.g., Statistics, Economics, Mathematics) or equivalent practical experience.

  • Specialized Domain Expertise: Significant experience with auction dynamics, yield management, or supply-demand matching within a marketplace setting.

  • Statistical Rigor: Mastery of causal inference and experimental design, with specific experience solving for interference and network effects in marketplace environments.

  • Technical Stack: Expert proficiency in Python or R, and advanced SQL.

  • Strategic Communication: Ability to translate complex statistical results into actionable business narratives for stakeholders at all levels of the organization.

  • Netflix Culture: A self-starter who thrives in an environment of radical transparency and high autonomy. You are a mentor and a collaborator who prioritizes the inclusion of diverse perspectives to reach the best possible decisions.

Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $372,000.00 - $600,000.00. This compensation range will vary based on location.

Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.

Netflix is a unique culture and environment. Learn more here.

Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

Job is open for no less than 7 days and will be removed when the position is filled.

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Netflixについて

Netflix

Netflix

Public

An online streaming platform that enables users to watch TV shows and movies.

10,001+

従業員数

Los Gatos

本社所在地

$280B

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

2.8

報酬

4.2

企業文化

3.9

キャリア

3.8

経営陣

3.2

68%

知人への推奨率

良い点

Great benefits and compensation

Innovative and diverse culture

Supportive team and management

改善点

Fast-paced and high pressure environment

Work-life balance issues and long hours

High workload and expectations

給与レンジ

1,877件のデータ

L6

Mid/L4

Senior/L5

L3

L4

L5

L6 · Lead Data Scientist

0件のレポート

$742,500

年収総額

基本給

-

ストック

-

ボーナス

-

$631,125

$853,875

面接レビュー

レビュー3件

難易度

3.7

/ 5

体験

ポジティブ 0%

普通 67%

ネガティブ 33%

面接プロセス

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

System Design Interview

5

Onsite/Virtual Interviews

6

Final Round

よくある質問

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit