Summary
A leading global investment bank is looking for an early-career Data Science & Analytics Analyst to sit at the intersection of quantitative analysis and live advisory work — a rare chance to shape both client deliverables and the firm’s internal AI platform from day one.
Company Information:
A leading global independent investment bank based in New York, recognized for pairing sharp advisory work with a growing, data-driven approach to client service.
Job Description:
Conduct exploratory data analysis, statistical analysis, and feature engineering for client projects and internal initiatives
Design, build, and improve models spanning prediction, classification, segmentation, and natural language processing
Use Python and SQL to pull, clean, reshape, and analyze data with speed and precision
Partner with senior team members on analytical workstreams tied to client mandates, strategic reviews, and business development efforts
Turn open-ended business questions into testable hypotheses, clear data requirements, and a sensible modeling approach
Produce analyses and visuals that can be shaped into polished, client-ready materials
Help develop the firm’s internal AI and analytics toolkit, including data pipelines, reusable workflows, and model-driven tools
Test and improve AI-powered workflows for knowledge retrieval, summarization, and classification
Keep clear, auditable records of methodologies, data sources, model logic, and results
Follow internal standards for model governance, data quality, explainability, and information security
Requirements / Qualifications:
Bachelor’s degree required, ideally in a quantitative discipline (Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or Finance)
Up to 2 years of experience in data science, analytics, quantitative consulting, or financial services
Required: Python and SQL
Working knowledge of standard data science libraries for analysis, modeling, and visualization
Exposure to machine learning methods such as regression, classification, clustering, time series, or NLP
Comfort working with both structured and unstructured data
Preferred: Git, notebook-based development, strong coding and documentation habits
A plus: hands-on exposure to LLMs, prompt engineering, or retrieval workflows; experience with Power BI, Tableau, or similar tools
