ML Engineer
ebay
San Jose
hace 4 días

Position Title : ML Engineer

It is all about knowing our customers. The customer science team is in the unique position to study eBay customers and our work helps eBay to deliver personalized experiences to our buyers and sellers.

We innovate rapidly in this space and there is no shortage of new challenges for motivated individuals.

As a candidate, you will be working with senior applied researchers on projects that deliver values to millions of eBay customers.

If you enjoy the scale and technical complexity of real world problems and want to be at the frontier of applied research in e-commerce, join us now.

NOTE this role will require someone with a strong background in Object Oriented programming (Java). This is NOT an Analyst role (Although Data analysis will be part of the role).

This is not a Recent Grad role

Responsibilities :

  • Work with data scientists to develop machine learning models and data pipelines to deliver insightful yet practical solutions and deploy those models into production (Java).
  • Conduct data analysis / visualization from multiple data sources to provide valuable insights for feature engineering
  • Define key metrics and build dashboards to monitor the performance and health of ML models in production
  • Basic Qualifications :

  • Solid Professional background in Software Engineering with a focus on Object Oriented, Back End Development required
  • Strong experience with Java and the Spring Framework is Required.
  • Experience in big data processing and analysis, e.g. Hadoop, SQL, Spark is required
  • MS or PhD in Computer Science, Statistics, Mathematics, or equivalent
  • Expected experience : PhD - at least 2 years of industrial / professional experience (Not counting Internships or Co-Ops) working in a production environment in a related field .
  • M.S. at least at least 4-5 years of industrial / professional experience (Not counting Internships or Co-Ops) working in a production environment in a related field .

  • Experience with one or more of the following : classification, regression, recommendation systems, targeting systems, ranking systems, fraud detection, online advertising, or related is a plus
  • Experience with site experimentation (A / B testing) is a plus
  • Enthusiastic collaboration and effective communication are essential
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