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Data Scientist (L5) - Content Promotion & Discovery Performance

Netflix

Data Scientist (L5) - Content Promotion & Discovery Performance

Netflix

USA - Remote

·

Remote

·

Full-time

·

2w ago

Compensation

$372,000 - $600,000

Benefits & Perks

Healthcare

Mental Health

401(k)

Equity

Flexible PTO

Healthcare

Mental Health

401k

Equity

Required Skills

Causal Inference

Statistical Analysis

Python

SQL

Experimentation Design

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.

The Merch & Algo Performance team focuses on how we can maximize member joy and engagement with our platform by improving and optimizing how we distribute titles on our product. We are looking for a talented Senior Data Scientist with strong statistics and causal inference experience to help us understand how content promotion and personalization algorithms come together. The distribution and promotion of our content as well as personalization are key to how members discover and choose content they would enjoy.

As a Senior Data Scientist, you’ll partner with the business leaders responsible for content & product promotion, Product, Engineering and AI for Member System teams and other diverse stakeholders across the business to shape strategy and deliver impact through analytics, experiments, causal inference and ML/AI methods.

In this role, you will:

  • Leverage causal inference methods or statistically sound analyses to drive actionable insights and help shape our understanding of how product, content and algorithms influence how members discover content.

  • Build metrics and measurement frameworks that are statistically and causally robust to empower decision-making for stakeholders and product features that can deliver member joy.

  • Establish strong partnerships with team members and diverse stakeholders across the business.

  • Facilitate ownership and accountability by ensuring that the team is producing trustworthy and high-quality outputs that influence the decisions and direction of a variety of areas of Netflix.

  • Proactively explore trends in content discovery to surface future opportunities.

  • Work independently and drive your own projects.

To be successful in this role, you have:

  • An advanced degree in Statistics, Mathematics, Computer Science, Economics, or a related quantitative field.

  • 5+ years of relevant experience in one or more data science roles.

  • Exceptional communication with technical and non-technical audiences; highly effective at developing meaningful stakeholder relationships and deep domain expertise.

  • Expertise in statistical analysis methods, most notably causal inference, statistical learning and experimentation methods.

  • Strong quantitative programming skills in a language such as Python and data manipulation in SQL.

  • Comfort with ambiguity; ability to thrive with minimal oversight and process.

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 $372,000.00 - $600,000.00. 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

Mid/L4

Mid/L4 · Analytics Engineer

7 reports

$274,996

total / year

Base

$211,536

Stock

-

Bonus

-

$274,996

$358,605

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