Data Scientist - Associate

You are a strategic thinker passionate about driving solutions in “Applied AI & Machine Learning”. You have found the right team.
As “Applied AI & Machine Learning Professional” within our “Asset Management Data Science Team ” , you will be responsible for setting and improving our organizational objectives, and ensuring their consistent accomplishment.
Job Responsibilities
Design and implement scalable architecture for LLM-powered investment tools, including reusable patterns for integration, governance, observability, and access control
Build and scale AI applications and automated workflows using models, retrieval, and tools with orchestration for state management, retries, checkpointing, deterministic controls, and appropriate human oversight
Develop context-management and retrieval-augmented generation (RAG) capabilities, including ingestion, chunking, metadata, embeddings, hybrid search, reranking, and grounded outputs
Create reliable data and knowledge pipelines that transform structured and unstructured enterprise content into governed, high-quality inputs for AI applications
Establish engineering standards for reusable tools and model integrations, including interfaces, permissions, testing, failure handling, and documentation
Implement evaluation and end-to-end observability for AI systems, including structured logging and tracing across model calls, retrieval steps, and tool execution
Optimize AI systems for quality, task completion, latency, token usage, cost, reliability, and business impact
Partner with portfolio managers and research teams to understand investment processes and translate them into practical solutions within the firm’s ecosystem
Required qualifications, capabilities and skills
Earn a Master’s degree or PhD in computer science, statistics, mathematics, engineering, econometrics, or another quantitative field
Apply a strong foundation in statistics, probability, experimental design, and machine learning to select appropriate methods and interpret quantitative results
Build, deploy, and scale LLM-powered applications, including retrieval, tool use, workflow orchestration, state management, structured outputs, and evaluation, using frameworks such as LangGraph, Semantic Kernel, LlamaIndex, or equivalent
Design prompts and context effectively, including embeddings, vector and keyword search, reranking, model selection, and quality/latency/cost optimization
Write strong Python and SQL, using common data and machine learning libraries and frameworks
Demonstrate sound numerical intuition and understanding of financial markets, investment research, portfolio construction, risk, performance, and investment data
Translate ambiguous business requirements into scalable technical solutions and communicate technical trade-offs to a range of stakeholders
Preferred qualifications, capabilities and skills
Deliver generative AI or machine learning solutions in a regulated enterprise environment
Bring front-office or buy-side experience, particularly in investment research, portfolio analytics, performance attribution, or decision analytics
Incorporate unstructured or alternative data into research and production workflows
Apply familiarity with Model Context Protocol (MCP) or comparable standards for securely connecting AI applications to enterprise data, tools, and services
Use LLM observability and evaluation standards or platforms such as OpenTelemetry, OpenInference, Arize Phoenix, LangSmith, or equivalent (including traces across model, retrieval, and tool-execution steps)
Leverage familiarity with knowledge graphs, multimodal models, fine-tuning, synthetic data, or advanced model-evaluation techniques
Pursue CFA qualification or progress toward the CFA designation