Top Data & Analytics Jobs

Conduct quantitative research for proprietary trading strategies by analyzing high-frequency market data, developing statistical and machine-learning models, building simulations and backtesting tools, and evaluating model performance. Collaborate with traders and developers to validate algorithms, improve pricing and execution, assess production readiness, prioritize research initiatives, and integrate validated models into low-latency trading systems. Mentor graduate researchers and interns.

Leads an enterprise organization spanning data and AI governance, architecture, and product management. Establishes governance frameworks, architectural standards, risk controls, and responsible AI practices while developing scalable data and AI products. Translates enterprise strategy into investment portfolios, roadmaps, operating models, and measurable outcomes. Advises executives, aligns business and technology stakeholders, manages budgets and vendors, and builds a multidisciplinary leadership organization focused on adoption, reuse, quality, compliance, and business value.

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.

Leads advanced analytics, reporting, scalable data models, dashboards, and data pipelines supporting Business and Marketing teams. Partners with senior stakeholders and AI Engineering to implement AI and machine learning workflows, predictive insights, and automation. Develops documentation, drives analytics engineering best practices, and mentors team members. Requires strong SQL, Python, APIs, data modeling, stakeholder communication, project leadership, and blockchain expertise.

Develop automated data-processing applications, algorithms, and machine-learning systems for F-15 flight-test engineering. Process, merge, visualize, and analyze aircraft and weapons-system data; perform troubleshooting and root-cause analysis; assess system performance against digital models; and collaborate with engineering teams on advanced radar, sensor, control, communications, and weapons technologies.

Leads BI and analytics development by designing, optimizing, and governing dashboards, analytical models, semantic layers, SQL queries, and stored procedures across Cognos, QlikView, and Power BI. Collaborates with business, data science, visualization, and engineering teams to prepare data, develop machine learning or exploratory analytics solutions, automate production workflows, and deliver actionable insights. Provides technical leadership, mentors team members, establishes standards, reviews work, and promotes reusable components and best practices.

Leads complex fulfillment-center analytics and applied AI initiatives from problem definition through production adoption. Builds scalable data models, pipelines, inspection tools, root-cause analysis, predictive solutions, and self-service analytics. Partners with Product, Engineering, Data Science, and Operations, establishes engineering standards, conducts reviews, mentors junior engineers, and communicates recommendations to senior stakeholders. Requires strong SQL, Python, cloud data, machine learning, visualization, testing, and software engineering expertise.

Lead data architecture across more than 100 applications, establishing architecture principles, database standards, data modeling practices, governance frameworks, and data exchange patterns. The role drives migration to strategic cloud data lakes, reduces duplicate data platforms, defines target-state architectures, serves as a subject matter expert, and mentors junior architects. It requires extensive experience with cloud databases, data lakes, data governance, data quality, analytics, machine learning, and distributed enterprise technology environments.

Deliver business intelligence and performance insights for institutional sales teams. Build sales enablement tools, AI-enabled workflows, KPI frameworks, and scalable data models. Analyze complex datasets, prototype statistical or machine learning models, ensure data governance, and translate findings into recommendations for senior stakeholders while partnering with technology and cross-functional teams.

Design, build, test, and support scalable cloud data pipelines and platforms using Python, SQL, Spark, databases, and cloud data technologies. Develop data models, distributed and streaming workloads, reusable data products, and end-to-end data solutions. Enforce engineering design patterns, security controls, encryption, and access management. Collaborate with product managers, engineers, analysts, and data scientists while mentoring developers and communicating technical outcomes to stakeholders.

Design, build, deploy, and support scalable cloud data solutions, pipelines, platforms, and applications for enterprise risk management. The role uses Python, Spark, SQL, relational and NoSQL databases, Databricks, Snowflake, and cloud technologies. Responsibilities include data modeling, distributed and streaming workloads, security controls, architecture standards, observability, operational efficiency, stakeholder communication, mentoring, and collaboration with product, engineering, analytics, and machine learning teams.

Data Engineering Interns will develop and maintain scalable data pipelines, APIs, data products, and enterprise platforms. Responsibilities include data ingestion, transformation, validation, governance, quality monitoring, metadata management, cloud and distributed computing, CI/CD, testing, and observability. Interns will also use approved generative AI tools, support AI-enabled engineering workflows, and collaborate with engineers, architects, product teams, and business stakeholders. The 10-week hybrid program includes training, mentorship, and potential consideration for a subsequent full-time Technology Analyst role.

Data Engineering Interns will build and maintain scalable data pipelines, APIs, data products, and enterprise platforms. Responsibilities include data ingestion, transformation, validation, governance, quality monitoring, metadata management, cloud and distributed computing, CI/CD, testing, and observability. Interns will also use approved AI tools, support AI-enabled engineering workflows, and collaborate with engineers, architects, product teams, and business stakeholders. The 10-week program includes technical training, mentorship, and networking, with potential consideration for a full-time Technology Analyst role.

Data Engineering Interns will build and maintain scalable data pipelines, APIs, data products, and enterprise platforms. Responsibilities include data ingestion and transformation, software testing, CI/CD, cloud and distributed computing, data quality, governance, metadata, lineage, and reliability initiatives. Interns will also use approved AI tools, support AI-enabled engineering workflows, collaborate with technical and business teams, and participate in training, mentorship, and networking during the 10-week program.

Leads an analytics team supporting Home Lending Operations by framing business problems, delivering analysis, building reporting, defining data requirements, and advising senior stakeholders. Develops AI and machine learning use cases to optimize operations and customer experiences, establishes analytics quality standards, supports experimentation and product measurement, and partners cross-functionally with Operations, Product, Technology, Finance, Strategy, Business Intelligence, and Modeling teams.

Manage data analytics for PULSE Risk & Compliance by analyzing large datasets, identifying transaction anomalies and risk exposure, validating models, improving reporting and processes, and supporting compliant data strategies. The role involves Python-based programming, BI, database and data-storage design, strategic testing, cross-functional collaboration, project leadership, and people management in a hybrid environment.

Build and maintain scalable data pipelines, data applications, planning tools, APIs, and automated quality processes for network capacity planning. Develop ETL/ELT workflows, anomaly detection, monitoring, metadata and lineage solutions, and machine learning or AI-enabled applications. Analyze data, optimize performance, document systems, and collaborate with engineering, product, operations, software development, and data science teams to deliver reliable production solutions.

Owns complex GenAI Standards of Care evaluations, reviewing architectures, data sources, testing, controls, and governance evidence. Identifies risks and control gaps, provides remediation guidance, supports governance dashboards, escalates systemic issues, and improves governance processes and documentation. The role partners with technical teams, solution owners, and leadership to ensure GenAI use cases meet enterprise standards and manage data and technology risks.

Develop causal economic analyses using Mastercard and public datasets. Translate business questions into empirical designs, apply econometric and machine learning methods, lead projects from data preparation through validation, guide causal inference practices, prototype scalable analytics, and communicate findings to clients, stakeholders, and nontechnical audiences.

Develop causal economic analyses using Mastercard and public datasets. Translate business questions into identification strategies, empirical designs, and causal effects; lead projects from data ingestion and feature engineering through model validation and stakeholder communication. Apply econometric, causal machine-learning, and experimentation methods, guide scientific best practices, prototype scalable analytics, and present technical findings to clients and non-specialists.

Analyze complex datasets, build data models, create automated Looker visualizations, and deliver actionable insights for business decisions. The role partners with IT, engineering, product, and business stakeholders to improve data pipelines and processes, applies statistical and machine learning techniques, and mentors junior analysts. The position requires strong SQL, data visualization, programming, cloud-platform, communication, and data storytelling skills.

Leads quantitative analytics initiatives that transform complex data into actionable insights and scalable, governed solutions. Responsibilities include stakeholder partnership, self-service analytics development, customer journey analysis, AI and automation application, executive presentations, production reporting support, regulatory compliance, process improvement, and mentoring junior analysts.

Early Career Analyst supporting Affirm’s Risk & Analytics organization through data analysis, portfolio monitoring, reporting, risk and growth strategy insights, and cross-functional collaboration. The role uses SQL and Python to build scalable reports, support machine learning and risk model deployment, assess lending regulations, and improve product launches and market expansion. Analysts will integrate AI tools into workflows while validating outputs and developing expertise in consumer lending and quantitative business decision-making.

Develop market intelligence, forecasting, statistical models, scenario analyses, and automated reporting for supply chain decisions. Integrate internal and external datasets using Python, R, SQL, and other tools; build dashboards; support data governance; and translate analytical findings into recommendations for procurement, planning, and executive stakeholders.

Leads Corporate Responsibility data strategy, governance, ownership, integration, protection, quality, and risk management. Partners with business, technology, analytics, risk, legal, compliance, and data teams to deliver trusted data assets and platforms supporting operations, reporting, advanced analytics, machine learning, and responsible AI. Establishes accountability, prioritizes remediation, reports governance metrics to senior leadership, and manages direct and matrixed teams.