Machine Learning Scientist 5 - Localization

Location: New York, USA - Remote

Remote Type: Onsite

Time Type: Full time

Job Description

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 Localization Data Science and Engineering team is at the forefront of removing language barriers and providing a stellar member experience to all our members regardless of their language preferences. We are responsible for the translation and cultural adaptation of all aspects of member interaction, including beautiful localized user interfaces, subtitles, and dubbing of award-winning Netflix originals.

We are looking for an experienced Machine Learning Scientist to join our growing team. In this role, you will build causal and machine learning models to evaluate the impact of localization algos, partner with teammates to support localization algo strategy, and train supervised ML models for localization use cases. You will also partner with a talented cross-functional team of engineers, scientists, product managers, and domain experts to shape localization strategy and deliver business impact.

Responsibilities

• Act as strategic partner for researchers and engineers to guide localization algo development

• Define and execute on roadmaps for measuring localization member impact and improving localization member experience with Causal Inference and Machine Learning tools

• Partner closely with other business leaders, product managers, and other data scientists to refine and scale your findings

• Present your research and insights to all levels of the company

• Become a regional expert on Localization Data Science and Engineering, helping educate and connect with regional offices

About you

• Proven track record of researching and leading Causal Inference, Machine Learning, and AI Evaluation methods in ambiguous and complex areas with a focus on technical rigor and robustness

• High proficiency in standard tech stack (e.g., R, Python, SQL), Causal Inference (e.g., propensity score matching, double machine learning), and Machine Learning (Supervised Learning, LLM Evaluation methods)

• 4+ years of relevant experience with Causal Inference and Machine Learning applications

• Exceptional communication and collaboration skills coupled with strong business acumen

• Comfortable with ambiguity; able to take ownership, and thrive with minimal oversight and process

• Netflix culture resonates with you

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.