Job Description:

Lead Search Scientist - Toronto, ON Canada

At eBay Classifieds Group (ECG) we are re-inventing the way we approach and tackle information retrieval and findability within our local businesses to revolutionize the way we connect buyers and sellers.

The Lead Search Scientist will be instrumental in the shaping and execution of our vision, working closely with a specialized group of engineers and architects as strategic member of the FiSci (Findability Science) Team.

This is a greenfield opportunity in the online classifieds space that will require tenacity, collaboration, experimentation and creativity to redefine the way we approach and execute search science within ECG. As the global leader in online classifieds with major properties including Kijiji Canada, Gumtree UK, and Marktplaats NL, the research and solutions of the FiSci team will affect millions of users at large scale across the globe.


Responsibilities

Researching and Identifying the most effective data models and techniques to support advanced information retrieval across ECG, including Relevancy Feedback, Natural Language Processing and Recommendation Systems

Working closely with senior engineers and architects in the development and testing of your models and techniques to bring them to life into a complete search and recommendation pipeline

Employing A/B testing and formal statistical methods to validate and test decisions and implementations

Following lean engineering principles, iterating often, incrementally and effectively

Communicating and defending your ideas and findings through presentations and data visualization when appropriate

Quarterly global travel to train, interface and engage with ECG local business leaders in the domain of FiSci.


Preferred Qualifications

Proficient in statistical methods of inference, modelling and experimentation

Familiarity with the Vector Space/TF-IDF information retrieval model and Elastic Search and/or Lucene

Someone who loves to innovate, be a thought leader and is driven by solving tough problems

Comfortable debating their own ideas and those of others to arrive at correct decisions, not compromises.

Design and implementation experience of low latency information retrieval systems supporting real time indexing


Job Requirements

Master’s degree in Applied Mathematics or Statistics

Proven success of employing Supervised and/or Unsupervised Machine learning techniques at scale for information retrieval or recommender systems

Proficiency working with Big Data systems over HDFS such as Hadoop or Spark

Statistical programming experience in an environment such as R or Octave

Experience applying Natural Language Processing techniques (online and offline) to query shaping and other areas of information retrieval such as clustering and classification