Job Summary

As part of the Data Science and Quality Assurance team, you will play a hybrid role focused on both ensuring the quality of Koïos products and contributing to the creation of high-quality datasets used to train and evaluate machine learning models. You will work at the intersection of software testing and AI data development, helping to improve the reliability, performance, and intelligence of our conversational AI platforms.

Responsibilities

Quality Assurance

• Collaborate with cross-functional teams, including developers, product managers, and business analysts, to define and create comprehensive test plans that align with project goals and objectives.

• Execute test cases manually and, when applicable, through automated testing tools. Report and track defects, providing detailed information to assist in their resolution.

• Conduct regression testing to validate that new code changes do not adversely impact existing functionality.

• Maintain accurate and up-to-date documentation of test plans, test cases, and test results. Generate test summary reports and communicate testing progress and findings to the team.

• Actively participate in process improvement initiatives to enhance the efficiency and effectiveness of the quality assurance process.

• Work closely with developers and product teams to provide feedback and contribute to the resolution of defects and issues.

• Ensure that all testing activities adhere to established quality standards and best practices.

• Identify and communicate potential risks associated with product quality, proposing strategies to mitigate them.

• Review the intent generation and support the team in optimising data generation processes.

• Analyse user feedback and activity, and iterating to enhance the user experience.

• Conduct targeted user surveys to study their behaviour with new features developed by Koios. Provide market analysis to the business team.

Data Generation & Annotation

• Generate synthetic data that closely mimics real-world scenarios for training machine learning models.

• Annotate and label large datasets with accurate tags for various features and classes to aid in model training and testing.

• Review and validate data annotations to ensure consistency and accuracy across the dataset.

• Collaborate with data scientists and machine learning engineers to understand data requirements and deliver datasets that meet specific model needs.

• Utilize annotation tools and software, adhering to project-specific guidelines and protocols.

• Participate in the development and refinement of annotation guidelines and quality control procedures.

• Identify and report any issues that may affect data quality or integrity.

• Stay updated with the latest trends and technologies in data annotation and machine learning datasets.

Requirements

• Bachelor’s degree in a related field (Computer Science, Linguistics, Business Administration, or equivalent experience)

• Minimum 2 years of experience in software quality assurance, data annotation, or a related role

• Strong analytical and problem-solving skills

• Excellent attention to detail and commitment to quality

• Strong communication and collaboration skills

• Knowledge of software development methodologies and the software development life cycle

• Familiarity with machine learning concepts and the importance of high-quality training data

• Ability to manage multiple tasks and prioritize effectively

Nice-to-Have

• Experience with test automation tools such as Selenium, Appium, or similar

• Knowledge of programming languages such as Python or Java for test automation

• Familiarity with continuous integration, continuous delivery (CI/CD) processes, and QA automation practices

• Experience with bug tracking and test management tools (e.g., Jira, TestRail)

• Experience using data annotation platforms and tools

• Understanding of data privacy, ethics, and responsible AI practices

• Exposure to diverse data types (text, audio, image, etc.)