Lead QA Engineer - Automation:
We are looking for a candidate who will be an agent of change and who will drive and execute based on our Quality and Reliability Transformation Roadmap. He/She will be responsible for testing Education Service domains across platforms, automation of functional/regression and RFB (sanity) service test suites, Continuous Integration/Continuous Delivery pipeline and Shift Left methodologies. We will also expect the candidate to take ownership of a functional domain and play a hands-on role in the execution of testing for projects in this area.
The candidate should have extensive experience in testing, testing automation, testing leadership and software development. The candidate should be proficient in-service automation testing tools like Eclipse, IDNE, Jenkins, GIT hub and Chatbot testing like Botium. The candidate should have at least 7+ years of service testing or software development experience.
The candidate should be flexible, highly adaptable and an excellent team player. The candidate is expected to work with Agile teams that consist of test analysts, developers, automation engineers and other stake holders. The candidate should expect to work in a global virtual team, sometimes across multiple time zones. The candidate may be assigned to more than one role or project at a time.
Lead QA Engineer - Automation Responsibilities:
Lead QA Engineer - Automation Requirements:
A Bachelor's degree in Computer Science or related field
Senior Data Scientist-Job Description
The Senior Data Scientist role is a creative problem solver who utilizes statistical/mathematical principles and modelling skills to uncover new insights that will significantly and meaningfully impact business decisions and actions. She/he applies their data science expertise in identifying, defining, and executing state-of-art techniques for academic opportunities and business objectives in collaboration with other Analytics team members. The Senior Data Scientist will execute analyses & outputs spanning test design and measurement, predictive analytics, multivariate analysis, data/text mining, pattern recognition, artificial intelligence, and machine learning.