Position: Trainee QA Engineer with basic Machine learning knowledge)
Company: GlobalLogic
Category: QA
Experience required: no_exp
English level: intermediate

**Job Description**
The candidates must meet the requirements:
- Ready to working day with day or evening shifts
- Good theoretical knowledge in the quality assurance field;
- Good applied statistics skills, such as distributions, statistical testing, regression, etc.
- Analytical and accurate;
- Ability to work in a multi-site distributed team and on a cross-platform development environment
- Highly motivated and proactive team member
- Good written and spoken English;
- Basic experience in working with any bug-tracking system.

**Nice to have:**
- Basics of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, GBM, xGBoost, etc
- Basics of data mining, data visualization
- Basic experience with common data science toolkits, such as R, Python.

**Job Responsibilities**
A successful candidate would become a part of an experienced team implementing different commercial solutions in the digital analysis area. The team is responsible for the development of effective solutions to streamline the processes, improvements to documentation workflows, changes to the architecture design.
 
The candidate will be responsible for:
Participate in full-cycle development of web-based commercial solutions.
Review of system requirements and test designs as input for implementation;
Work with a Bug-tracking system;
Verify of bug fixes;
Provide status reports to the QA Lead;
Communicate to team members and customers;
Be a part of the team, improve team practices and approaches.
Department/Project Description
You will be focused on R&D projects for a multinational conglomerate corporation operating in the fields of industry, worker safety, health care, and consumer goods. As part of the GlobalLogic team, you will contribute to ML frameworks and tools enabling delivering innovative technology best-in-class solutions for our client.