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Applied Scientist

Job ID: 2698463 | Twitch Interactive, Inc.

DESCRIPTION

If you are interested in this position, please apply on Twitch's Career site https://www.twitch.tv/jobs/en/

About Us:

Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day.
We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and Twitter,  and discover the projects we’re solving on our Blog. Be sure to explore our Interviewing Guide to learn how to ace our interview process.

About the Role:

We are looking for an Applied Scientist to solve challenging and open-ended problems in the domain of recommendations, search, ranking and information retrieval. As an Applied Scientist on Twitch's Community team, you will use ML to help viewers find streamers and communities they’ll love. You will collaborate with a team of passionate scientists and engineers to develop these models and put them into production, where they can help Twitch's creators and viewers succeed and build communities.

You will report to the Applied Science Manager on the Community Discovery Team. This position is located in San Francisco, CA.

You Will:
- Develop and Productionize ML algorithms for recommendations, ranking and search problems that can improve discovery on Twitch.
- Collaborate with our Product and Engineering teams to work backwards from customer discovery problems, to determine the ML solution (algorithm and pipeline) to have the biggest impact on our user base in the real world.
- Participate in the scientific community at Twitch, Amazon, and the broader ML and risk community.

Perks
- Medical, Dental, Vision & Disability Insurance
- 401(k)
- Maternity & Parental Leave
- Flexible PTO
- Amazon Employee Discount

BASIC QUALIFICATIONS

- BS in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, or equivalent relevant industry experience. We welcome applicants with non-traditional backgrounds.
- Hands-on experience with predictive modeling and analysis to solve real-world problems.
- Proficiency with Python and Deep Learning Frameworks such as PyTorch or TensorFlow, basic proficiency with SQL.

PREFERRED QUALIFICATIONS

- MS or Ph.D. in relevant field or a Degree with a specialization in Machine Learning.
- Experience shipping your ML models to production.
- 1+ years of experience working in the recommendations or search.
- Papers in top ML conferences of journals such as NeurIPS, RecSys, ICLR, ICML etc.
- Familiarity with Twitch, its business, and its community.

We are an equal opportunity employer and value diversity at Twitch. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, veteran status, or disability status, or other legally protected status.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $129,400/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.