大手監査法人グループでの不正対策コンサルタント（品質保証プラットフォームエンジニア / 品質相互検証エンジニア）の求人

There are two major functions of quality engineering which the candidate may select:

Platform:
o The engineer will be expected to create, track, report and execute test cases, for the platform or client dependences to maximize high SLO’s such as 99.999%+.
o The engineer will be expected to achieve 90%-100% automated test coverage for the platform using various tools. Adding functions such as alerts.

Scientific:
o The engineer will be expected to confirm data science measurements and prove the numbers visually with error measurements (e.g. Statistical P-Value, False Positive Rate, Precision/Recall, etc.).
o The engineer will be expected to achieve 90%-100% automated test coverage for any automated analytics dashboards, reports and alerts.

General:
o Work heavily with quantitative Quality KPI’s which may differ by client or organization. This includes, but is not limited to the Service Level Agreement (SLA), test coverage, etc.
o Evaluate 3rd party systems, consult in the area of quality with external clients, and/or apply their quality skills in the platform or scientific area of their choose.
o The actual quality engineering work may be either manual or automated test.
o Supporting engineering, security, ops on 24x7 call.
o Program Management such as technical presentations, documentation, ticket creation and tracking, scheduling, budget, and reporting.
o Senior and Management positions are also considered based on experience.

Experience:
B.S. and M.S degree CS.
English fluency required.
Prior experience in Fraud, Payments, Fintech, Finance, Biotech, Bioinformatics, Internet, Health or Medical fields.
7+ years QA experience in Japanese companies.
Quantitative, metrics, and KPI driven, expert presentation skills, expert prioritization.
Experience in either Platform Experience or Scientific Experience below.

Platform Experience:
Experience attaining a Service Level Objective up-time >= 99.999%
Golang, Java, Python, Javascript, Typescript, Kotlin
Cypress, Pytest, Postman, Loadrunner, Selenium, Testcafe, Jetfire, Jenkins
UAT, ETL, SQL, NoSQL, REST
PCI DSS, GDPR, CMMI
Public Cloud, On-premise and Hybrid environments.
Georeplication, microservices, mesh, edge, serverless, deployment.

Scientific Experience:
D3.JS, Golang, Java, Python
Pytest, R, Tableau, Excel
Applied Statistics, Optimization and Error Measurements such as Statistical Moments, Kurtosis, Kernel Regression, and other statistical equations and distributions used for confirming numerical measurements.
Measures of Fit, P-VALUE, Mean Squared Error (MSE), Root Mean Squared Error (RMSE)
K-Fold, Confirmation Bias, Pearson Correlations
Data Access, Data Preparation, Data Storage, ETL
Custom Scientific Visualization
Public Cloud