Senior Machine Learning Scientist, Conversion Modeling
Roku, Building C
Teamwork makes the stream work. Roku is changing how the world watches TV
Roku is the #1 TV streaming platform in the U.S., Canada and Mexico, and we've set our sights on powering every television in the world. Roku pioneered streaming to the TV. Our mission is to be the TV streaming platform that connects the entire TV ecosystem. We connect consumers to the content they love, enable content publishers to build and monetize large audiences, and provide advertisers unique capabilities to engage consumers.
From your first day at Roku, you'll make a valuable - and valued - contribution. We're a fast-growing public company where no one is a bystander. We offer you the opportunity to delight millions of TV streamers around the world while gaining meaningful experience across a variety of disciplines.
About the team
The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers and Roku. The systems and solutions span across different disciplines and technologies to perform real-time multi-objective optimization with distributed systems at large scale and low latencies. We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems and Auction Dynamics to solve a large set of complex problems. At the core of this is our Machine Learning, Experimentation and Inference Platform that powers the entire landscape which we continuously evolve over time.
About the role
In this role you will work on applying SOTA research and conduct your own research to develop novel methodologies to solve a large variety of challenging problems in Advertising related to conversion modeling aligned with attribution methodologies/models, calibration, dynamic creative generation and optimization, forecasting and timeseries modeling, yield and margin optimization and Experimentation for A/B and multivariate testing.
We’re looking for a strong technical leader with a solid grasp of core statistical techniques and deep experience in SOTA Deep Learning discriminative and generative models.
What you’ll be doing
- Building generative models to generate image and video ads geared towards optimizing performance
- Stay at the forefront of advancements in related areas
We’re excited if you have
- PhD in a quantitative discipline such as CS, Statistics, Applied Math or a related field
- 8+ years of experience in applied research using statistical and deep learning techniques
- Published paper(s) on deep learning models for Advertising or related areas
- Excellent communication and collaboration skills
Preferred Qualifications
- Experience in the Advertising domain
- Contributions to open-source ML projects
Our Hybrid Work Approach
Roku fosters an inclusive and collaborative environment where teams work in the office Monday through Thursday. Fridays are flexible for remote work except for employees whose roles are required to be in the office five days a week or employees who are in offices with a five day in office policy.
Benefits
Roku is committed to offering a diverse range of benefits as part of our compensation package to support our employees and their families. Our comprehensive benefits include global access to mental health and financial wellness support and resources. Local benefits include statutory and voluntary benefits which may include healthcare (medical, dental, and vision), life, accident, disability, commuter, and retirement options (401(k)/pension). Employees are supported in taking time off, in accordance with local leave policies and other personal needs to support their evolving work and life needs. It's important to note that not every benefit is available in all locations or for every role. For details specific to your location, please consult with your recruiter.
Accommodations
Roku welcomes applicants of all backgrounds and provides reasonable accommodations and adjustments in accordance with applicable law. If you require reasonable accommodation at any point in the hiring process, please direct your inquiries to View email address on click.appcast.io.
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