Lead QA Engineer (Data)

The job overview for the Lead QA Engineer position at Allergan Data Labs is as follows: Allergan Data Labs is seeking an experienced Lead QA Engineer to join their Data Science team and contribute to the transformation of the Allergan Aesthetics beauty business at AbbVie. The successful candidate will work within a startup-oriented environment while having the support of a large pharmaceutical company. Reporting to the QA Manager, the Lead QA Engineer will collaborate with key stakeholders to solve critical technical problems and will be responsible for testing intelligent data-driven systems, creating and maintaining test automation frameworks, and organizing user testing across various internal services and data points.

• Focused in testing intelligent data-driven systems testing
• Write primarily complex database test queries in SQL (Complex Joins, etc.)
• Create and maintain test automation framework related to testing Database based tests
• Organize and execute user testing that spans across an array of internal services and data points
• Collaborating to define & invent quality assurance environments with testing strategies for proprietary software systems
• Document Test plans and communicates a QA strategy for the Product Engineering team
• Establish a process for reporting, organizing, and prioritizing issues

• 4+ years in a QA Leadership role, with a strong background in QA planning, process, techniques, and tools
• 2+ years of experience in Great Expectations
• 2+ years in testing heavily of enterprise-grade ML platforms
• 2+ years writing Automated Python-based blackbox tests
• 2+ years of using Containers (Docker etc.)
• Experience in testing and validation of Data Warehouse tools like Snowflake
• Strong SQL is a must, using DBs such as: SQL, Postgres, MongoDB, Oracle, etc.
• Familiarity with common metrics for ML quality such as accuracy, precision, recall, F1-score, AUC, ROC, etc
• Experience with data augmentation, synthetic data, and similar techniques for improving model quality and testing scalability
• Experience with instrumentation and performance profiling of ML models with large memory requirements and tight response tolerances
• Strong Analytical skills and dealing with intricate system integrations
• Understanding of A/B testing
• Working experience with Amazon Web Services is required
• Knowledge and experience with different types of testing strategies
• Experience with application monitoring and logging tools (Datadog, CloudWatch, etc.)
• Good understanding of probability and statistics and their applications to AI/ML software quality evaluation
• Experience using and testing with Workflow management applications such as Airflow orchestration tool
• Completed graduate course(s) in statistical computing, machine learning, advanced experimental design, and multivariate analysis

• Competitive salary
• Competitive annual bonus targets
• 401k with dollar for dollar match, up to 6% of eligible earnings
• RSU grants (Long Term Incentives) for approved roles
• Comprehensive medical, dental, vision, and life insurance
• 17 paid holidays per year, including 3 floating holidays
• Annual Paid Time Off (PTO), with separate sick days
• 12 weeks paid Parental Leave
• Caregiver Leave
• Adoption and Surrogacy Assistance Plan
• Flexible workplace accommodations
• Opportunities to attend Lakers, Knicks, Anaheim Ducks, Anaheim Angels, and NY Rangers games
• Opportunities to attend concerts, festivals, and other live entertainment events
• Tuition reimbursement
• Attend a tech or marketing conference of your choice each year
• A MacBook Pro and accompanying hardware
• A modern productivity toolset (Slack, Miro, Loom, Lucid, Google Docs, Atlassian, etc.)
• Generous discounts on SkinMedica skin care products
• Discounted aesthetic treatment days multiple times a year
• $600 worth of Alle benefits each year to use towards aesthetic treatments and products
• Eligible for donation matching to over 1.5 million nonprofit organizations
• Attend AWS Re:Invent in person (Las Vegas) or virtually each year (for certain roles)