Top Entry Level Data & Analytics Jobs in NYC, NY

Analyze large, complex client and channel datasets to identify behavioral patterns, segments, journeys, and opportunities across banking touchpoints. Apply statistical and quantitative techniques, build reusable analytical assets, and translate findings into executive-ready narratives and actionable recommendations. Partner with business, strategy, technology, risk, and analytics teams to improve client experiences and business outcomes while maintaining data, privacy, and quality standards.

Performs quantitative catastrophe risk analyses using existing models to estimate client exposure and potential losses from natural and man-made catastrophes. Reviews and validates client exposure data, identifies abnormalities, prepares reports and presentations, communicates with clients on routine projects, and researches scientific and industry information on regional perils.

Build modern analytics infrastructure, self-service AI analytics tools, foundational datasets, and business data pipelines. Partner with teams to track product metrics, support financial and marketing reporting, identify cost savings, and improve business decisions through strategic analysis and consulting.

Join LSEG’s 12-month graduate programme in Data and Analytics, developing foundational knowledge of financial markets, data, and business operations. You may align with Analytics or Data and Feeds based on your skills and interests. Responsibilities include analyzing information, drawing conclusions, proposing solutions, collaborating on projects, and adapting in a fast-paced environment. The role offers permanent employment, professional development, industry qualifications, hybrid working, and participation in global induction and assessment activities.

Design and maintain ETL workflows, data models, and pipelines supporting business intelligence and reporting. Work with Redshift, NoSQL, AWS, and data integration tools to ensure reliable, governed data. Build dashboards in Omni, Mode, and Mixpanel, analyze finance and accounting data, and partner with Finance, Product, Operations, and Engineering to translate business needs into actionable insights and promote data-driven decision-making.

Own Qualitate’s data engineering foundation by building pipelines for structured and unstructured data, designing relational and non-relational data models, and developing AI-powered enrichment workflows. Establish data quality monitoring, integrations, schemas, contracts, lineage, and documentation. Partner with Applied AI, Research, and Engineering while shaping the company’s early data architecture and enabling customer data integrations.

Own daily data operations, including data collection, pipeline monitoring, quality validation, root-cause investigation, SQL-based cleaning, and Metabase dashboards. Partner with engineering and deployment teams to resolve data issues, standardize metrics, document processes, and build tooling that improves reliability and scalability for government clients.

Own subscription marketing and lifecycle analytics for acquisition, conversion, retention, churn, CRM campaigns, engagement, attribution, cohorts, and spend efficiency. Write SQL, build dashboards, investigate data quality issues, evaluate offers and experiments, and translate findings into recommendations for marketing and cross-functional stakeholders.

Manage and match sensitive administrative data to create analytic datasets for research and evaluation projects. Review, adapt, document, and maintain SAS code; develop data dictionaries; document dataset creation and governance processes; identify documentation gaps; and support privacy, confidentiality, security, data quality, transparency, and reproducibility requirements while collaborating with research teams.

Analyze and improve structured and unstructured financial data pipelines using Python, SQL, statistical methods, machine learning, and LLMs. Responsibilities include data cleansing, validation, anomaly detection, reconciliation, dataset onboarding, API integration, model evaluation, workflow monitoring, documentation, and root-cause investigation. The role also involves explaining technical findings, managing assigned datasets, and supporting reliable production data processes in a quantitative trading environment.