Data Scientist

A Career with Point72’s Technology Team As Point72 reimagines the future of investing, our Technology team is constantly evolving our firm’s IT infrastructure and engineering capabilities, positioning us at the forefront of a rapidly evolving technology landscape. We’re a team of experts who experiment and work to discover new ways to harness open-source solutions, modern cloud architectures, and sophisticated Artificial Intelligence (AI) solutions, while embracing enterprise agile methodologies. Our commitment to building and innovating in the AI space provides the framework intended to drive smarter decision making and enhance how we build and operate our platforms and applications. As a member of Point72’s Technology team, we encourage and support your professional development from day one—helping you advance your technical skills, contribute innovative ideas, and satisfy your own intellectual curiosity—all while delivering real business impact for our multi-billion-dollar global business. What you’ll do Lead the development and deployment of advanced models and algorithms that turn complex data into actionable insights to influence decisions across the organization Build and champion the rollout of a technology insights product, setting clear service standards, aligning stakeholders, and establishing transparent metrics to measure impact and drive adoption Design and maintain a centralized analytics platform that unifies key performance indicators, satisfaction scores, and operational metrics into intuitive dashboards for leadership Develop automated data pipelines and validation processes to gather, clean, and prepare large sets of structured and unstructured data for modeling and analysis Partner with data engineers, analysts, and business partners to translate business challenges into scalable, production-ready data solutions and shared standards Create reports and drill-down analyses that highlight service health, enable targeted action planning, and support proactive management Monitor and analyze performance across service quality, project manager satisfaction, efficiency, operational risk, and cost, highlighting trade-offs and providing strategic recommendations Use historical trend analysis and experimentation to uncover recurring issues, measure the impact of corrective actions, and drive continuous improvement Integrate third-party data sources and application programming interfaces into the analytics ecosystem to expand capabilities and enrich models Explore and implement modern cloud-native and distributed computing tools and methodologies to improve scalability, reliability, and reproducibility What’s required 5–10 years of professional experience in data science or a closely related field in financial services or technology environments ...