Teams at Gradient AI

Build and maintain automated data quality testing for customer data pipelines. Responsibilities include developing dbt, Soda, and SQL checks; validating schemas, loads, sequencing, and downstream reconciliations; detecting anomalies; managing severity levels and customer overrides; reporting quality metrics; and partnering with engineering teams to embed testing into new pipelines. The role requires strong backend testing, SQL, Python, Airflow, and Databricks experience, along with excellent debugging and communication skills.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Own project and case management across health underwriting products, coordinating intake, prioritization, tracking, dependencies, risks, and resolution. Partner with Product, Data Science, Data Engineering, Implementation, QA, and client teams to remove blockers and improve accountability. Build reporting on workload, ticket aging, bottlenecks, and resolution rates while connecting Salesforce, Jira, and spreadsheet workflows to automate processes and create a proactive, data-driven operating model.