Machine Learning Scientist (L5) - Member Lifecycle and Monetization DSE

Netflix is one of the world's leading entertainment services, with 283 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.

The Member Lifecycle and Monetization Data Science & Engineering team plays a critical role for Netflix in driving and accelerating sustainable growth of members and revenue globally, by leveraging data, experimentation & machine learning to develop compelling and persuasive conversion and monetization experiences post-signup to optimize revenue per member. Machine Learning in these areas is a relatively greenfield area, and comes with the potential for 0-1 applications that can drive millions of dollars of impact at Netflix’s scale. 

We are looking for an experienced ML Scientist to join the team to innovate on core foundational lifecycle ML models, partnering with other scientists and cross-functional partners in product. We are looking for someone who will spot gaps in how we’ve done things before, and find better ways to do them. 

In this role, you will: 

• Own and innovate on our suite of foundational lifecycle models, such as lifetime value and other churn models.

• Have end-to-end ownership of ML model development lifecycle, from feature engineering, to model training, evaluation, deployment and continuous monitoring and improvement.

• Closely work with Data Science and Product stakeholders to develop a roadmap that meets the needs of changing business priorities.

• Communicate complex concepts with both technical and non-technical stakeholders to influence strategic decisions.

• Advocate for and apply best practices around developing and deploying data-driven applications

• Live Netflix values in daily work and interactions

What you’ll bring:

• Deep experience developing and deploying Machine Learning models with a successful track record of and passion for delivering business impact.

• Exceptional communication skills, able to explain complex technical concepts clearly to cross-functional partners 

• Ability to deal with ambiguity; a strong ownership mindset, and a desire to thrive on minimal oversight and process

• You have deep familiarity with the ML lifecycle and strong technical judgment when assessing different solutions for deploying models in production.

• You have strong coding experience in Python, and expertise in ML frameworks (e.g., scikit-learn, Keras, PyTorch, TensorFlow)

• You hold an advanced degree (MS or PhD) in Computer Science, Electrical Engineering, Statistics, Computational Social Science, or a related technical field with a focus on machine learning, artificial intelligence, and predictive modeling.

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 $170,000 - $720,000.

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.

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

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity of thought and background builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national or