Headline

Risk Analyst @GoldmanSachs | Ex-WellsFargo | Agentic Orchestrations| Advanced RAG| Python | SQL

About

I am a Risk Analyst at Goldman Sachs, focused on leveraging quantitative methods and data-driven analysis to strengthen control frameworks and improve capital efficiency. My work centers on: • Risk quantification and scenario analysis • Data modeling and control effectiveness analytics • Process optimization through Python-based automation • Translating risk insights into actionable business decisions I am particularly interested in the intersection of risk analytics, statistical modeling, and revenue optimization, where disciplined risk frameworks support sustainable performance and strategic capital deployment. Technical strengths include Python (pandas, numpy), SQL, and data visualization tools, with a focus on building scalable analytical solutions rather than manual reporting.

Location

Bengaluru

AI stack

anthropic apis

claude code

github copilot

hugging face

mcp

notion

Work experience

Risk Analyst at Goldman Sachs, Nov 2025 - Present · 11 mos, Bengaluru, Karnataka, India

Artificial Intelligence Engineer at Goldman Sachs, Nov 2025 - Present · 11 mos, Building an agentic analytics engine over non-financial risk data (events, RCSA, metrics, issues): normalizes and embeds unstructured event descriptions, then applies Leiden/Louvain greedy modularity-based community detection to build hierarchical graph clusters analogous to a Neo4j knowledge graph, surfacing unique control-gap and remediation themes per division. Deployed a validation agent that iteratively re-runs clustering to maximize modularity and coherence, then exposed the graph as a live analytics substrate for an LLM agent that crawls it to answer ad hoc RFIs and surface cross-division risk patterns — replacing manual analyst triage with agent-driven graph traversal. Designed and deployed MCP servers exposing internal risk datasets and regulatory tools as live structured context for LLM agents, enabling grounded, real-time reasoning in production workflows. Built a LangGraph multi-agent orchestration framework (retrieval, reasoning, validation, summarization agents) for end-to-end diagnostic automation across large regulatory datasets; used Redis for distributed agent session state and semantic caching. Cut large-document processing latency 50–65% via async inference pipelines on AWS (Lambda, EC2, S3); built LLM evaluation frameworks for factual grounding and output consistency, adopted as a governance-aligned quality standard.

Analytics Consultant at Wells Fargo, Jul 2023 - Nov 2025 · 2 yrs 5 mos, Bengaluru, Karnataka, India

Education

Bachelor of Technology - BTech, Chemical Engineering, National Institute of Technology Karnataka