Senior Associate, Risk Analytics

The Role: Senior Associate, Risk Analytics

Gemini is seeking an Sr. Associate, Risk Analytics to join the Risk Management function. As we evolve our risk infrastructure, we are looking for a quantitative and technically proficient individual to move our risk management from reactive, manual processes to a proactive, automated, and agentic model. This role will own the design and implementation of systems for continuous control monitoring, using agentic AI to detect, escalate, and remediate risk across the entire enterprise.

You will work closely with Engineering, Data, Compliance, and Operations to build the "risk brain" of the company—an environment where risk controls are codified, monitored in real-time, and scaled through autonomous agents.

Responsibilities:

Continuous Control Monitoring
Lead the design and implementation of an automated, continuous monitoring framework that spans all enterprise risk domains (Credit, Fraud, Operational, Compliance). Move the firm away from periodic/manual checks toward real-time observability.
Agentic Risk Infrastructure
Scale our risk management capabilities by developing and deploying AI agents to monitor control activities. Orchestrate agentic workflows to perform proactive anomaly detection, perform root-cause analysis on incidents, and suggest risk mitigation actions.
Enterprise Risk Analytics
Interpret large, complex datasets to formulate problem statements and generate insights across all business verticals. Develop predictive models and detection tools that reduce risk exposure and enhance operational efficiency.
Risk Governance & Reporting
Maintain a comprehensive view of the AI and model risk inventory. Develop automated dashboards that provide senior management with a "solid analytical story," leveraging BI tools to track Key Risk Indicators (KRIs) against appetite.
Cross-Functional Collaboration
Partner with the Risk Engineering, Identity, and Product teams to ensure risk telemetry is properly instrumented. Provide clear communication and training on how to interpret agentic outputs and maintain human-in-the-loop oversight.

Minimum Qualifications:

Bachelor’s degree in a quantitative field (e.g., Computer Science, Statistics, Finance, Engineering).
3–5 years of experience in risk management, analytics, or a related technical domain.
Technical Proficiency: Strong SQL and relational database skills; intermediate-to-advanced Python ability.
AI/Agentic Frameworks: Demonstrated experience or strong familiarity with building or deploying AI agents (e.g., LangChain, AutoGen, or similar orchestration frameworks) and large language model integration.
Continuous Monitoring: Experience designing or maintaining automated systems for data quality, observability, or control monitoring.
Data Communication: Ability to translate complex data and AI output into clear, actionable reporting for senior stakeholders.
Mindset: A "no-task-is-too-small" attitude, combined with the ability to operate in ambiguity and a healthy balance of empathy and skepticism toward automated systems.

Preferred Qualifications:

Experience with Business Intelligence and Data Visualization (Tableau, Looker).
Prior experience in model risk management (MRM) and knowledge of MAS AI MRM supervisory expectations.
Experience with AWS Bedrock, GCP and opensource LLMS.
Experience in MLOps or AIOps (e.g., CI/CD pipelines for AI/ML models).