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Research Scientist 5 - Member Understanding Foundations

Netflix

Research Scientist 5 - Member Understanding Foundations

Netflix

USA - Remote

·

Remote

·

Full-time

·

2mo ago

Compensation

$466,000 - $750,000

Benefits & Perks

Healthcare

Mental Health

401(k)

Equity

Flexible Hours

Parental Leave

Healthcare

Mental Health

401k

Equity

Flexible Hours

Parental Leave

Required Skills

Python

Machine Learning

PyTorch

TensorFlow

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.

As Netflix expands into delivering a broader range of entertainment options and business models, it is increasingly important that we build a holistic understanding of who our consumers are and how they experience our product. We are seeking a Research Scientist to support the vision, understanding, and development of Machine Learning algorithms that provide insights into our consumers. This high-impact role will be pivotal in uncovering opportunities, influencing product decisions, and informing future directions. The ideal candidate will excel in opportunity discovery, cross-functional collaboration, and have a passion for leveraging their ML expertise to continuously improve our understanding of our members.

Responsibilities:

  • Research, develop, and iterate on machine learning models that improve Netflix’s understanding of our members.

  • Drive your own roadmap, determining what areas are best served by rapid iteration and which ones deserve deeper development.

  • Collaborate with Data Scientists, ML Scientists, and Engineers on a variety of projects to ensure we maximize our ability to understand our members.

  • Stay up to date with research in areas related to Sequential models, LLMs, and identifying when and where to implement new best practices and methodologies.

  • Present your research and insights to all levels of the company, in both technical and non-technical settings.

About You

  • You have substantial, relevant industry experience in building scalable ML models and translating this expertise into business impact.

  • You hold an advanced degree (PhD) in Computer Science, Electrical Engineering, or a related technical field with a focus on machine learning, artificial intelligence, or computer vision, or equivalent industry experience.

  • You have strong software engineering skills in Python and are proficient in using frameworks such as Py Torch or Tensor Flow.

  • You are flexible with your approach, able to pivot between research goals and implementing end-to-end solutions with ease.

  • You excel at solving complex problems with innovative solutions, developing novel algorithms, and adapting existing methods from the literature to new challenges.

  • You are an excellent communicator, capable of explaining complex technical details to both technical and non-technical audiences.

  • You thrive in fast-paced, dynamic environments with ambiguity, making positive contributions to team collaboration and company culture.

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 $466,000.00 - $750,000.00. 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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About Netflix

Netflix

Netflix

Public

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

10,001+

Employees

Los Gatos

Headquarters

$280B

Valuation

Reviews

4.2

15 reviews

Work Life Balance

4.2

Compensation

4.5

Culture

3.2

Career

3.8

Management

3.0

65%

Recommend to a Friend

Pros

Very high compensation packages (430k-700k)

Fully remote work opportunities

All cash compensation structure

Cons

Lower compensation than expected in some cases

Difficult interview process

Simple/uninteresting technical problems

Salary Ranges

1,869 data points

L3

L4

L5

L6

Mid/L4

Senior/L5

L3 · Data Scientist

0 reports

$242,500

total / year

Base

-

Stock

-

Bonus

-

$206,125

$278,875

Interview Experience

4 interviews

Difficulty

4.0

/ 5

Offer Rate

25%

Experience

Positive 25%

Neutral 25%

Negative 50%

Interview Process

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

System Design Interview

5

Behavioral Interview

6

Team Matching

7

Final Round

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit