Currently tracking 11 active AI roles, up 109% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $124k–$310k (avg $233k).
| Title | Stage | AI score |
|---|---|---|
| Analyst II, Full Stack This role focuses on developing and optimizing fraud decisioning strategies within a fintech company. It involves extensive data analytics, collaborating with cross-functional teams (Product, Engineering, Operations, Finance), and developing new fraud features. A key responsibility is creating scalable frameworks for proprietary fraud machine learning models and evaluating data sources to mitigate fraud risk. The role also involves partnering with the Machine Learning team on fraud and identity verification strategies and owning the end-to-end analytics workflow, including defining metrics and creating dashboards. | Data | 7 |
| People Knowledge Experience Manager This role focuses on building a centralized Employee Experience (EX) Hub, leveraging AI to enhance support and knowledge management within the People Operations function. The primary goal is to establish an AI-ready knowledge ecosystem, design integrated systems and workflows, and implement AI-enabled tools for efficient and compliant employee support, while managing operational and regulatory risks in a regulated HR environment. |
| Data |
| 5 |
| Staff Analytics Engineer, Subledger Platform Staff Analytics Engineer to build and own the Financial Subledger Data Platform using dbt and Snowflake. This role involves creating dbt models, implementing data quality and controls, and embedding AI-assisted reconciliation capabilities. The engineer will also own subledger data products, partner with accounting teams, drive operational ownership, and collaborate with upstream engineering teams. The role includes coaching another engineer. | Data | 5 |
| Staff Analytics Engineer, Subledger Platform Staff Analytics Engineer role focused on building and owning the Financial Subledger Data Platform using dbt and Snowflake. The role involves creating dbt models, implementing data quality and controls, and embedding AI-assisted reconciliation capabilities. It requires strong SQL, data modeling, Python, and Snowflake expertise, with a focus on production ownership and mentoring. | Data | 5 |