Client's Machine Learning group works on machine learning and statistical analysis problems across several areas of the company. We take an entrepreneurial approach to our projects, identifying high value opportunities and building solutions in a collaborative team environment. These include a diverse set of problems such as: personalization, review fraud detection, search / navigation, machine vision, TV analytics, automated content curation, and CRM. We use advanced techniques in natural language understanding / NLP, recommender systems, learn-to-rank models, statistical inference, social network analysis, and deep learning. We are looking for experienced machine learning engineers and data scientists with broad knowledge of machine learning techniques to design and implement machine learning solutions throughout the company.
You'll be working with a multidisciplinary team of smart people including data scientists, developers, and product managers.
You'll have an amazing amount of data as raw material for your projects, including over 780 million collected reviews and opinions, 5 terabytes of log data per day, and 300,000 photos and videos per day.
You will be testing your hypotheses and product online on our 455M monthly unique visitors.
You must be comfortable communicating your approaches to project managers across the company.
PhD (preferred) or Masters in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Operations Research or similar field OR 5 years industry experience in same.
Excellent communications skills
A passion for solving real world problems with machine learning
Experience working with natural language, machine vision, collaborative filtering, clustering, classification, regression, information retrieval, and/or statistical modeling.
Associated topics: chief program officer, cpo, manage, manager, management, monitor, product manager, project manager, relationship manager, task
* The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.