Top Data & Analytics Jobs

Senior Solution Architect responsible for strategy, solution and data architecture across application, platform, and data domains. Designs scalable architectures, data models, integration patterns, and data platforms (warehouses, lakes, streaming, cloud). Partners with business and engineering teams to ensure architectures meet functional, nonfunctional, governance, security, and regulatory requirements while enabling analytics, AI/ML, and transformation.

A 10-week quantitative analytics internship for PhD candidates. Interns apply statistics, artificial intelligence, machine learning, and programming to credit risk, fraud detection, operational loss, portfolio forecasting, model validation, and consumer decisioning projects. The role includes technical training, mentorship, collaboration with quantitative professionals, and exposure to senior leaders. High-performing interns may be considered for full-time roles after graduation.

Leads international CRM decision intelligence, translating customer, campaign, and market data into strategy and next-best-action recommendations. Oversees predictive analytics, segmentation, measurement, dashboards, data quality, privacy, opportunity sizing, and AI-enabled insight generation across global markets. Partners with CRM, data, AI, digital, commercial, legal, and compliance teams to scale trustworthy personalization and analytics.

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Build and launch benchmark, thematic, factor, strategic beta, and multi-asset indexes from concept through market launch. Conduct quantitative research, data analysis, signal testing, back-testing, portfolio construction, risk modeling, and statistical testing. Use Python and AI tools to automate workflows and accelerate methodology development. Create prototypes, dashboards, research materials, methodology documents, and client-facing narratives while partnering with research, product, technology, operations, and commercial teams.

Automate recurring reports and dashboards, develop Tableau-based solutions, implement machine learning in business processes, and deliver data-driven solutions with stakeholders. Present analytical findings to senior leaders, publish data tables, and manage cross-functional projects. Requires advanced statistical modeling, SQL and Python data manipulation, enterprise tool development using Python and Excel, and project management using Monday.com.

Design and maintain Python-based analytics frameworks for KYC quality assurance testing, statistical sampling, validation, and workflow automation. Integrate data from internal systems and APIs, apply statistical and machine learning techniques, identify control gaps, and improve testing efficiency. Document methodologies, assumptions, controls, and limitations for audit and regulatory review. Partner with risk, compliance, operations, and quality assurance stakeholders while communicating analytical findings and methodology decisions clearly.

The Director of Business Development will define, negotiate, and secure partnerships to expand adMarketplace's search and performance advertising across various platforms while ensuring alignment with company goals and operational integration.

Build and maintain analytics data systems and pipelines that capture, transform, and integrate product usage data at scale. Deliver accurate insights and visualizations to inform product decisions, collaborate cross-functionally, and apply AI tools to improve analytics and workflows.

Lead design, validation, and monitoring of fraud strategies using data analysis and AI/ML. Partner with product, technology, and operations to develop detection capabilities, test implementations, and balance fraud mitigation with revenue, cost, and customer impact.

Lead data science projects to optimize drug development using AI. Develop models for patient selection and clinical trial evaluations while collaborating with cross-functional teams to drive insights.

The Manager of Regulatory Submissions will lead regulatory strategies and submissions for global authorities, ensuring regulatory alignment with business goals while collaborating with cross-functional teams on medical devices.

Develop and implement software solutions for automated trading systems, working with quantitative researchers on complex financial challenges.

Design, develop, and deploy software solutions for automated trading systems while collaborating with researchers on technology priorities.

As a PhD intern, you'll design, develop, and deploy software solutions for automated trading, collaborating with the Quantitative Research team.

Support risk and analytics efforts by performing SQL/Python-based analysis, monitoring portfolio health, building scalable reports, collaborating cross-functionally, and integrating AI tools to surface insights that inform product, risk, and growth decisions.

Design, build, and productionize batch and streaming ETL/ELT pipelines and ML workflows on Azure Databricks. Implement Unity Catalog data products, MLflow model lifecycle controls, CI/CD for data/ML artifacts, and operational monitoring. Partner with cross-functional teams to modernize legacy workflows into scalable, production-ready solutions.

Lead data ownership and governance for Corporate Finance and Treasury: define business data meaning, drive data strategy execution, improve data quality and controls, manage delivery timelines, mitigate data risks across the lifecycle, and support analytics by governing data used in analytics platforms.

Lead data ownership and governance to deliver curated, reusable data products. Partner across business, technology, analytics, and risk to define metadata, lineage, quality rules, and lifecycle management. Drive data integration, mitigate data risks, enable analytics/ML use cases, automate governance with AI/LLMs, and manage delivery, remediation, and stakeholder relationships to ensure scalable, discoverable, and enterprise-fit data products.

Lead data ownership and governance for wholesale credit risk. Define data strategies, ensure data quality, metadata and lineage, enable data products, mitigate data risks, and coordinate cross-functional delivery for analytics and reporting.

Drive card fraud strategy through data analysis, trend monitoring, and analytics-driven recommendations. Partner with product, technology, and operations to design, test, and implement detection and treatment solutions while balancing fraud mitigation, financial performance, customer experience, and operational efficiency.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

Lead and scale the data engineering organization to build data products and architecture that power GTM (marketing and sales) strategy. Define technical strategy, enable reporting, analytics, data science, ML/AI workloads, and partner with IT, product, analytics, and GTM stakeholders to deliver production-grade data platforms and services.

Curate, cleanse, validate, and maintain large US equity and options datasets; integrate fundamental and alternative data; build data pipelines for research and trading; perform statistical and ML analysis to identify market patterns; document data provenance and collaborate with engineers and traders to deploy production-grade models.