Data Scientist

Responsibilities
Generative AI, RAG & LLM Evaluation: Design and productionize enterprise GenAI solutions using LLMs, advanced RAG, embeddings, vector search, GraphRAG, prompt/context engineering, and robust RAG evaluation frameworks covering groundedness, faithfulness, relevance, hallucination, and retrieval quality.
Agentic AI & Intelligent Automation: Build AI agents and multi-agent systems with reasoning, planning, tool calling, orchestration, memory/state management, human-in-the-loop controls, and guardrails to automate complex financial and enterprise workflows.
Digital Twins, Simulation & Synthetic Data: Develop digital twins and simulation frameworks for what-if analysis, forecasting, stress testing, scenario modeling, and optimization using Monte Carlo methods, synthetic data, statistical modeling, and machine learning.
Advanced Machine Learning & Data Science: Develop predictive and prescriptive solutions using machine learning, deep learning, NLP, time-series forecasting, anomaly detection, experimentation, optimization, and advanced statistical modeling to solve complex investment, client, risk, and operational problems.
Responsible AI, Governance & Model Risk: Establish responsible AI practices covering explainability, fairness, bias, privacy, security, model validation, AI guardrails, regulatory compliance, and model risk management appropriate for a highly regulated financial-services environment.
MLOps, LLMOps & Technical Leadership: Lead AI solutions from proof-of-concept through enterprise production, implementing MLOps/LLMOps, observability, model and prompt monitoring, drift detection, CI/CD, cloud deployment, and performance optimization while mentoring data scientists and partnering with engineering, business, investment, risk, and technology teams.

Work arrangement
Yes