Research Scientist 6 - Ad Marketplace

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.

We launched a new ad-supported tier in November 2022 and are building an in-house world-class ad tech ecosystem to offer our members more choices in consuming their content. Our new tier allows us to attract new members at a lower price point while also creating a compelling path for advertisers to reach deeply engaged audiences.

Our Team:

The Ad Marketplace team within Ads Data Science and Engineering plays a crucial role in the growth of Netflix's ad business. The mission for this team is to build a healthy, competitive, and innovative ad marketplace that balances long-term Netflix revenue, member experience, and advertiser outcomes. The team will be responsible for ad auction design, dynamic pricing, member ad experience, and inventory / yield optimization. Our goal is to create innovative, data-driven solutions that deliver highly relevant ad experiences for our members and achieve impactful results for advertisers, all while upholding the exceptional quality and personalization characteristic of the Netflix experience.

Responsibilities:

• Design and implement machine learning and optimization algorithms to improve ad quality and performance.

• Build, train, and evaluate models on large-scale production data.

• Develop online and offline evaluation frameworks to rigorously measure the impact of improvements to models and algorithms.

• Partner closely with the product team to define optimization objectives, constraints, and trade-offs that align with product and business goals.

• Communicate technical decisions, trade-offs, and experiment results to both technical and non-technical stakeholders, driving understanding and adoption of ML-driven solutions.

• Provide technical guidance and direction to the team 

Qualifications:

• Advanced degree (PhD or Master’s) in Computer Science, Statistics, Mathematics, or related quantitative field.

• Proficiency in Python, Scala or Java.

• Deep knowledge of machine learning, optimization, Auction design, pricing, and data analysis techniques.

• Experience with prototyping and deploying algorithms using large-scale production data.

• Strong business acumen and ability to translate technical results into business impact.

• Experience in ad optimization stack, e.g. targeting, ranking, bidding..

• Excellent communication and collaboration skills.

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.