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Our Commerce Data Science team works with a broad range of collaborators and stakeholders, including Product, Engineering, and Finance, to inform commerce strategy and data-informed decision-making through analysis, metrics research, experimentation, causal inference, and modeling, but also through exceptional judgment, partnership, and business acumen.
As part of our investment in next-generation commerce and identity capabilities, the Identity team is responsible for building a unified, individual-level identity platform that will serve as the core foundation for future product experiences. This cross-functional, cross-domain effort will reshape how we understand our users and how identity signals are captured, connected, and used across the Netflix ecosystem. Our mission is to deliver a more consistent, accurate, and personalized experience for every individual who interacts with Netflix. We are hiring founding data scientists to help define this space from the ground up and develop the scientific frameworks that will power new commerce experiences at global scale.
As a Data Scientist, you will join a team of other stunning Data Scientists, Analytics Engineers, Machine Learning Scientists, and Research Engineers. You will use data to identify opportunities and help shape product decisions.
In This Role, You Will
- Effectively identify and leverage experimentation and other analysis techniques, as appropriate, to solve business problems
- Be a strategic thought partner with commerce, product, engineering, analysts, data engineering etc
- Influence decision-making through data-driven insights and recommendations
- Proactively identify and perform data exploration to inform future product directions
- Prioritize competing demands toward the most impactful items
- Develop and validate the appropriate metrics to measure success in a given product area
- Contribute to the excellence of experimentation methodology within the Netflix data science community
- Live Netflix values while bringing a new perspective to continue improving our culture
- An advanced degree (Masters or PhD) in Statistics, Mathematics, Computer Science, Economics, or a related quantitative field
- Strong statistical skills such as regression and A/B testing experimentation
- Strong data manipulation and quantitative programming skills in SQL and Python or R
- Experience designing experiments and familiarity with observational causal inference methodologies
- Exceptional communication with technical and non-technical audiences
- Willingness to learn and rapidly absorb business context
- Comfort with ambiguity; ability to thrive with minimal oversight and process
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.
Netflix has 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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