Top Data & Analytics Jobs

Develop scalable data pipelines and data systems supporting economic insights, analytics, and machine learning. Build and optimize solutions using Python, Spark, Hive, Impala, Hadoop, and Databricks while helping migrate on-premises infrastructure to the cloud. Design data architectures and schemas, improve system performance and reliability, write tested code, and collaborate with data scientists, product managers, and infrastructure teams throughout the development lifecycle.

Leads an analytics team supporting commercial aviation aftermarket strategy. Oversees enterprise dashboards, forecasts, analytical models, data governance, KPI development, market intelligence, business cases, and strategic opportunity analysis. Manages request prioritization, quality assurance, delivery timelines, team capacity, hiring, coaching, and performance. Partners with business development and program teams to translate aerospace data and market trends into regional growth strategies and executive recommendations.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Leads Expedia Group’s data engineering, software engineering, AI, and innovation capabilities for Marketing Automation and MarTech. Sets enterprise technology strategy, oversees scalable data platforms, pipelines, APIs, integrations, governance, security, observability, and responsible AI foundations. Builds high-performing engineering teams, manages modernization and platform investments, enables analytics and experimentation, and partners with senior stakeholders to improve customer engagement, operational efficiency, and technology delivery.

Leads data engineering, data science, and AI initiatives, including Snowflake architecture, ETL pipelines, AWS data systems, data warehousing, analytics, and AI/ML solutions. Manages data governance, dashboards, experimentation, and web analytics integrations while building and mentoring multidisciplinary teams. Partners with Product, Editorial, Engineering, and business stakeholders to translate data needs into scalable solutions and drive business impact.

Leads quantitative model risk analysis for loan loss forecasting and allowance for credit losses. Evaluates model soundness, performance under stress scenarios, regulatory alignment, and model risk. Develops alternative approaches, applies econometric, statistical, and machine learning methods, improves model frameworks, documents findings, and communicates complex results to management, regulators, and technical audiences. Collaborates with model development and risk teams while supporting continuous improvement and Agile delivery.

Validates and enhances balance sheet forecasting, stress testing, capital markets, and financial risk models. Develops quantitative and machine learning solutions, analyzes large datasets, performs model testing and benchmarking, documents findings, and communicates results to business leaders and technical stakeholders. Collaborates across teams to build cloud-based analytical solutions and improve risk management processes.

Build and operate scalable cloud data platforms and pipelines for Capital One’s Risk Tech organization. Responsibilities include designing distributed data solutions, enforcing engineering patterns, implementing security controls, collaborating across Agile teams, mentoring developers and analysts, and delivering AI-powered risk management products. The role requires expertise in Python, Spark, SQL, databases, cloud data warehousing, data modeling, and pipeline development.

Leads the Federal Reserve Bank of Boston’s Data & Analytics department, setting strategy, architecture, governance, and multi-year technical roadmaps. Oversees self-service analytics, data infrastructure, pipelines, curated datasets, visualizations, and decision-support tools. Builds stakeholder partnerships, ensures responsible AI use, manages operational risks and controls, and develops a high-performing multidisciplinary team. Represents the function to executive leadership and collaborates across the Federal Reserve System.

Own the analytics lifecycle from complex raw data through executive-ready insights. Design experiments, analyze performance drivers, build scalable data pipelines and analytical frameworks, and communicate findings to marketing, product, and technology stakeholders. The role supports customer acquisition, owned media optimization, repeatable reporting, and roadmap decisions through secure, maintainable SQL and Python or R solutions.

Develop and deploy advanced statistical and ML models on large-scale alternative datasets to generate investable signals, partner with investment teams, evaluate data quality, and drive novel data-driven research with commercial impact.

As a Quantitative Research Engineer, design, develop, and implement software for automated trading systems using advanced statistical techniques. Collaborate with researchers and prioritize business needs.

Evaluate Business Banking deposit and cash management products using data-driven analysis. Develop pricing strategies, analyze small-business banking behavior, create dashboards and reports, build data assets, automate processes, and provide insights for product development. Collaborate with Product, Design, and Technology teams, participate in senior product reviews, and present recommendations to business leaders.

Develop causal economic analyses using Mastercard and public datasets. Translate business questions into identification strategies, build econometric and causal machine-learning models, lead projects from data ingestion through validation, and communicate findings to clients and stakeholders. Guide best practices, prototype scalable analytics, conduct research, and apply experimentation methods across business contexts.

Develop causal economic analyses using Mastercard and public datasets to assess business decisions, market changes, events, and external shocks. Translate business questions into empirical designs, lead projects from data ingestion through model validation, prototype scalable causal inference methods, guide best practices, and communicate findings to internal and external stakeholders. The role requires econometric and causal machine-learning expertise, experimentation experience, and strong technical communication skills.

Designs and maintains data pipelines supporting monthly profitability close, automates manual finance workflows, and develops machine learning solutions for quality control, anomaly detection, and reconciliations. The role extracts and delivers financial data, prototypes LLM and agentic tools, and partners with Finance, Technology, and P&A on analytics initiatives. It also communicates technical methods and results to senior business stakeholders.

Leads global network economics and value optimization initiatives through data-driven pricing, interchange, revenue, and profitability analysis. Develops predictive models, simulations, segmentation frameworks, reporting, visualizations, data pipelines, and scalable analytics tools using large datasets. Partners with global and regional pricing teams, translates stakeholder needs into analytical solutions, presents actionable findings, manages project delivery, and mentors junior colleagues.

Analyze business data, uncover operational improvement opportunities, and develop automation and self-service solutions. The role partners with stakeholders and subject matter experts, performs ad hoc analysis, communicates actionable insights, and leads simplification projects. It requires experience with analytics and automation tools including Alteryx, Tableau, SAS, SQL, Python, Snowflake, AWS, APIs, and AI/ML concepts. This is an onsite role five days per week and does not offer visa sponsorship.

Leads a global organization spanning data science, data architecture and engineering, and reporting and insights. Develops enterprise data strategy, oversees data products and analytics initiatives, advances predictive and prescriptive insights, establishes governance and quality standards, and partners with executives to deliver measurable business value. Builds high-performing teams, develops leaders, prioritizes investments, and drives scalable, trusted, decision-oriented analytics capabilities.

Leads enterprise competitive intelligence capabilities, including data collection, matching, scraping, governance, quality standards, analytical methodologies, and AI-enabled workflows. Partners with Product, Engineering, Data Science, Merchandising, Pricing, Digital, Marketplace, and Strategy teams to translate business needs into scalable solutions. Develops metrics, models, validation processes, and insights that support business decisions, while potentially managing and developing a small analytics team.

Build and lead NVIDIA’s enterprise data governance platform, covering sensitive-information detection, governed access, export-control enforcement, audit logging, data connectors, and AI knowledge readiness. Design scalable backend, distributed, and data-processing systems; integrate enterprise content sources; evaluate ML classification models; enforce access-control fidelity; and ensure ingestion quality. Provide technical leadership through architecture decisions, code and design reviews, mentorship, and cross-functional alignment with Security, Legal, Finance, and platform teams.

Leads data ownership and governance for data products, defining metadata, quality, controls, integration, and risk mitigation. Partners with business, analytics, technology, and engineering teams to deliver curated datasets and production-ready solutions. Uses advanced SQL for validation and root-cause analysis, manages workstreams and staff, and supports analytics and machine learning use cases. Applies AI and LLM tooling to automate governance activities and maintains data integrity across regulated environments.

Designs and supports end-to-end BI solutions spanning data ingestion, modeling, semantic layers, dashboards, system integrations, governance, and data quality. Partners with data engineering and finance teams to optimize pipelines, improve reporting, and resolve integration issues. Supports advanced analytics and data science initiatives through data exploration, feature preparation, and model evaluation. Requires strong SQL, data warehousing, dimensional modeling, and project leadership skills, with preferred experience in ETL tools, governance frameworks, and modern analytics platforms.

Leads quantitative and qualitative market research on international development, AI adoption, institutional demand, funding, procurement, competition, and commercial opportunities. Evaluates AI and data service applications, develops market assessments and strategic recommendations, manages research initiatives, prepares executive presentations, and collaborates with leadership, technical, operational, and business development teams to support market entry and growth.

Leads the design and modernization of scalable data architecture for economic data platforms, including massive time-series datasets, archival documents, and research publications. Responsibilities include data modeling, metadata and governance design, storage and processing architecture, indexing, discoverability, security, lifecycle management, and API data access optimization. Partners with application architecture and cross-functional teams to deliver highly available, secure, public-facing data platforms supporting analytics and research.