Own the analytics lifecycle for Trade & Working Capital—shaping problems, testing hypotheses, and delivering production‑grade models that drive portfolio strategy from origination through distribution.

As Portfolio Analytics Development Lead within the Portfolio Management team, you will partner with Product, Sales, Risk, and Portfolio Management to build analytics applications that support portfolio management across origination, pricing, limit setting, renewals, and distribution. You will work across the data‑science lifecycle—from data acquisition and feature engineering to model development and insight delivery—framing business problems, testing hypotheses, and translating analysis into embedded solutions and clear recommendations within a global team.

Job Responsibilities

• Build analytical tools and interfaces to measure exposure, flows, and market drivers; enhance performance attribution; systematically surface opportunities and portfolio actions
• Translate business needs into requirements and technical designs; define, design, and develop automated data solutions to create analysis‑ready datasets and streamline reporting that produces actionable insights
• Rapidly prototype solutions, lead structured testing with clear success criteria, and drive initiatives from pilot to full rollout with appropriate approvals and controls
• Embed real‑time insights and model outputs into sales and product workflows with Technology and Product partners via APIs and flexible decision tools
• Present recommendations to management and business partners with strong data‑visualization practices; champion data quality, validation, lineage, and documentation standards
• Build evaluation packages (offline benchmarks, A/B or holdout tests) to demonstrate efficacy, reliability, and fairness; communicate results and go/no‑go decisions; stay current on AI/ML and act as an internal SME

Required Qualifications, Capabilities, and Skills

• Advanced degree (MS or PhD) in a quantitative/STEM discipline or equivalent industry experience
• Commercial experience applying advanced analytics to high‑impact use cases (e.g., semantic search, information extraction, question answering, personalization, classification, recommendation, forecasting)
• Proficiency in Alteryx, SQL, Python, and BI tools to automate data solutions and flexible reporting
• Solid grounding in ML fundamentals and practical implementations (e.g., time‑series analysis, clustering, decision trees, deep learning)
• Strong knowledge of NLP, language modeling, prompt engineering, and domain adaptation for LLM applications
• Track record of taking solutions from prototype to production, including structured testing with defined success criteria and change‑controlled implementation
• Ability to communicate to technical and non‑technical audiences

Preferred Qualifications, Capabilities, and Skills

• Experience in performance attribution or trading/decision analytics; front‑office finance experience
• Familiarity with incorporating unstructured data into portfolio analytics and product development
• Knowledge of the alternative data landscape
• CFA designation or progress toward it