Data Scientist Jobs

About The Position

Requirements

• Ph.D. or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, or a related quantitative field.
• 7+ years of experience in data science, machine learning, predictive analytics, or artificial intelligence.
• 4+ years of hands-on experience developing NLP and Generative AI solutions.
• Strong proficiency in Python and modern software development practices.
• Experience developing and deploying LLM-based applications using commercial or open-source models.
• Experience with Retrieval-Augmented Generation (RAG), vector databases, embeddings, and semantic search.
• Experience with prompt engineering, prompt evaluation, and LLM performance optimization.
• Strong understanding of machine learning algorithms, deep learning, statistical modeling, and model explainability techniques.
• Experience working with structured and unstructured data at enterprise scale.
• Experience collaborating with cross-functional stakeholders and communicating technical concepts to non-technical audiences.
• Strong knowledge of model governance, validation processes, and documentation standards.

Nice To Haves

• Experience designing and deploying AI agents and multi-agent systems.
• Experience with agent orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, CrewAI, Autogen, or similar technologies.
• Experience serving open-source LLMs using vLLM, Hugging Face, or equivalent inference frameworks.
• Experience with RAG evaluation frameworks such as RAGAS or other LLM evaluation methodologies.
• Experience with model monitoring, MLOps, and production AI deployment.
• Experience with cloud AI platforms such as AWS Bedrock, Azure AI, Databricks, Snowflake Cortex.
• Experience building document intelligence solutions involving PDFs, OCR, document extraction, knowledge extraction from images, and workflow automation.
• Experience within banking, financial services, fintech, insurance, or other regulated industries.
• Experience supporting Model Risk Management (MRM), model validation, audit reviews, or regulatory examinations.
• Familiarity with MCP (Model Context Protocol), tool calling frameworks, and AI workflow automation platforms.

Responsibilities

• Design, develop, and deploy production-grade Generative AI solutions using LLMs, RAG frameworks, AI agents, and workflow orchestration platforms.
• Build intelligent document processing capabilities for information extraction, summarization, classification, question answering, and conversational AI applications.
• Develop agentic workflows capable of autonomous reasoning, task execution, tool utilization, and multi-step decision support.
• Design and implement retrieval pipelines, vector search architectures, embedding strategies, and knowledge-grounded AI systems.
• Evaluate and improve LLM performance through prompt engineering, model benchmarking, hallucination reduction, and faithfulness testing.
• Build scalable AI solutions using modern frameworks and infrastructure including vLLM, LangChain, LangGraph, MLflow, Databricks, Snowflake, and cloud-native platforms.
• Perform exploratory data analysis, feature engineering, and statistical analysis to support machine learning and GenAI model development.
• Develop model monitoring, evaluation, and observability frameworks to measure quality, reliability, fairness, and operational performance.
• Collaborate closely with Model Risk Management (MRM), Compliance, Audit, Legal, and Information Security teams to ensure responsible AI deployment.
• Create technical documentation, model development artifacts, validation packages, and executive-level presentations.
• Partner with product managers, engineers, data architects, and business stakeholders to identify and prioritize GenAI opportunities.
• Stay current with advances in Generative AI, agentic systems, multimodal AI, foundation models, and emerging industry best practices.