Quality Test Engineer I

The candidate will join the Scoring QA team under Business Services and help advance the team’s existing automation frameworks. This role involves making sound architecture decisions, validating business requirements, assessing risks, troubleshooting complex edge cases, aligning with stakeholders, and taking accountability for production outcomes. Accountabilities: Business support through risk & fraud analysis and research. Monitoring daily testing to ensure products accuracy. Modify pre-designed queries to assess data for research purposes. Conducts routine risk and fraud review and verification and ensures corrections/clarifications are made in a timely manner. Run calibrations of risk and fraud policies to optimize false positive/fraud capture rate. Investigate, track, and report data issues. Utilize various data workflow management and analysis tools. Contribute to improving the QA process and quality standards. Design, develop, and maintain automated test scripts and frameworks using SDET practices to improve test coverage, reliability, and efficiency. Collaborate with developers, product owners, and data teams to validate AI/ML-driven features, troubleshoot defects, and ensure production releases meet quality and accuracy standards. To ensure high quality and accuracy is maintained through software release. To provide feedback to the development team and ensure the work is done according to the schedule and quality requirements. Determine testing tools and software that can be implemented to aid QA and maximize testing coverage. Diagnoses system problems using dumps, traces or other diagnostic tools. Apply AI and Machine Learning concepts to support QA activities, including test prioritization, anomaly detection, data validation, and analysis of product outcomes. Flexible to other duties as assigned Core Responsibilities: The primary responsibilities consist of the following areas, which allow us to stop production updates that could create adverse effects for clients: Daily monitoring for scoring products to identify unintended outcomes of new data builds or code. Running analysis to see how data build changes will affect specific customers. Testing data builds when data sources are added, or data roll ups change. Operate in various development environments (Agile, Waterfall, etc.) while collaborating with key stakeholders. Keep abreast of new technology developments. Qualifications: Education: Bachelor’s degree holder. Basic knowledge of software development methodologies (e.g., Agile, Waterfall). Basic knowledge of applicable development languages including but not limited to: Python, Java, Javascript, SQL. Familiar with HPCC ECL is a PLUS. Basic understanding of AI and Machine Learning concepts, including model outputs, data quality, validation techniques, and responsible use of AI tools. Experience or working knowledge of SDET practices, test automation frameworks, API testing, CI/CD pipelines, and scripting languages such as Python, Java, JavaScript, or SQL. Ability to analyze data identify patterns or anomalies, and use automation or AI assisted tools to improve testing efficiency and decision making. Experience in data visualization tools such as Microsoft PowerBI and Tableau. Basic knowledge of data manipulation languages. Ability and desire to learn new processes, tools, and technologies. Good research skills. Ability to clearly communicate and collaborate with cross functional teams to escalate major issues as needed.