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Senior Machine Learning Scientist, Conversion ModelingRoku • San Jose, Arizona, United States
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Senior Machine Learning Scientist, Conversion Modeling

Roku
  • US
    San Jose, Arizona, United States
  • US
    San Jose, Arizona, United States
Jetzt Bewerben

Über

Teamwork makes the stream work. Roku is changing how the world watches TV 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 optimisation 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 time‑series modelling, 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. For California Only – The estimated annual salary for this position is between $148,750 – $361,000 annually. Compensation packages are based on factors unique to each candidate, including but not limited to skill set, certifications, and specific geographical location. This role is eligible for health insurance, equity awards, life insurance, disability benefits, parental leave, wellness benefits, and paid time off. 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 its compensation package to support 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 is 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 EmployeeRelations@Roku.com. #J-18808-Ljbffr
  • San Jose, Arizona, United States

Sprachkenntnisse

  • English
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