Machine Learning Engineer

Minimum qualifications:

• 5 years of industry experience in a data scientist or machine learning engineer role.
  
• Experience with machine learning frameworks such as Tensorflow, Scikit-Learn.
  
• Experience in design, implementation, and delivery of scalable build/test/release agile software development cycle.
  
• Experience with data processing and management with Relational Database Management System (RDBMS) such as Postgres, MySQL, and big data stacks such as Apache Spark.
  

Preferred qualifications:

• Experience in full-stack development for leveraging machine learning solutions.
• Experience with cloud platforms such as Google Cloud Platform (GCP).
• Familiarity with front-end development (e.g., D3.js, React JS).
• Excellent written and verbal communication skills to translate technical solutions and methodologies to executive leadership.
  
• Excellent programming skills in at least one of the general programming languages: Python, Java, Scala, C++.

Experience with cloud platforms such as Google Cloud Platform (GCP).

About the job

As a Quantitative Analyst, you will be responsible for analyzing large data sets and building expert systems that improve our understanding of the Web and improve the performance of our products. This effort includes performing complex statistical analysis on non-routine problems and working with engineers to embed models into production systems. You will manage fast changing business priorities and interface with product managers and engineers.

As a Machine Learning Engineer, you will work on solving technical issues across multiple business areas (e.g., Ads, YouTube, Search, Play, etc.) through business generation. You will collaborate with data scientists, analysts, and Program Managers to create data solutions to enable our finance partners to make informed decisions, manage risks and opportunities.

Responsibilities

• Work cross-functionally with data scientists, data engineers and program managers to understand, implement, and deploy machine learning pipelines.
• Improve machine learning scalability, usability, and performance.
• Explore technologies to deliver business benefits.
• Communicate results to peers and leaders.
• Advocate processes, standards, and engineering practices.

Advocate processes, standards, and engineering practices.Advocate processes, standards, and engineering practices.