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Netflix

Manager of Games Portfolio - DSE

Reposted 23 Days Ago
Be an Early Applicant
Remote
Hiring Remotely in USA
360K-920K Annually
Senior level
Remote
Hiring Remotely in USA
360K-920K Annually
Senior level
The role involves leading a team to develop data strategy, build dashboards, drive forecasting, conduct market analysis, and influence senior leadership for Netflix's games portfolio.
The summary above was generated by AI

Netflix is one of the world's leading entertainment services, with over 300 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time.

Data Science and Engineering (‘DSE’) at Netflix is aimed at using data, analytics, causal inference, machine learning (ML), and sciences to improve various aspects of our business. We are looking for a Manager of Games Portfolio DSE to lead a group of Data Scientists and Analytics Engineers who are essential partners to our Games Finance & Strategy, Licensing & Acquisition, and Senior Leadership teams, bringing consistency and structure to how we evaluate and manage our entire portfolio of games. You will lead a team that provides the data strategy, forecasting, and insights needed to inform our most critical business decisions.

To succeed in this role, you’ll need a blend of data science and analytics engineering expertise, coupled with exceptional business acumen. You should have a mindset focused on standardization, scalability, and systematic frameworks, and the ability to influence senior leaders across different disciplines. Your leadership will be key to ensuring the team provides the insights that shape our overall games strategy and drive our business forward.

Key Responsibilities:

  • Develop the data strategy for the games portfolio.

  • Lead a team in building and maintaining core dashboards, establishing consistent frameworks for title performance reporting, and creating an effective metric ecosystem across all games.

  • Drive forecasting and enable slate optimization by guiding the development and evolution of forecasting models for engagement and performance that inform critical business decisions.

  • Oversee the creation of tools for slate planning and optimization, and provide strategic support for content investments and resource allocation.

  • Serve as the primary source of audience intelligence by partnering with Finance & Strategy and Consumer Insights to conduct competitive market analysis, audience research, licensing intelligence, and other strategic analyses to position Netflix Games competitively.

  • Foster cross-domain collaboration by partnering with sister DSE teams across content categories (TV/movie and Live) to create a unified understanding of members across streaming, live events, and games.

  • Lead causal research to measure the lift and engagement between different media types.

  • Influence senior leaders by acting as a critical thought partner and ensuring data drives key decisions.

  • Coach and inspire a high-performing team, providing guidance and mentorship to ensure scalable, consistent, and high-impact insights.

Who Will Succeed in This Role:

  • Demonstrated experience building, managing, growing, and inspiring data science and analytics teams, with 5+ years managing highly technical individual contributors.

  • Possesses deep expertise in data science, analytics engineering, and business analytics, with a focus on standardization, scalability, and systematic frameworks.

  • Brings exceptional business acumen and the ability to influence senior leaders across various disciplines and business domains.

  • Prioritizes business impact and team success above all else, creating an environment where the team can lead and excel, with no room for ego.

  • Demonstrates strong interpersonal and communication skills, with a proven ability to influence both technical and business senior leaders.

  • Is comfortable with ambiguity and serves as a role model for the team through a consistently positive and collaborative approach.

  • Holds a Ph.D. or advanced degree in Economics, Statistics, Mathematics, Business Analytics, or a related field.

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 $360,000 - $920,000

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 detail about our Benefits 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.

Top Skills

Analytics
Data Science
Forecasting Models
Machine Learning

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