Top Data & Analytics Jobs

Summer Analyst internship for students pursuing a bachelor’s or graduate degree. Quantitative strategists apply advanced mathematics, programming, logical reasoning, research, and analytical methods to construct quantitative models supporting global financial markets and core financial operations. Interns receive training, attend orientation, and work on real responsibilities alongside experienced professionals in a fast-paced environment.

Leads claims-focused insights, analytics, business intelligence, and AI initiatives for an insurance organization. Partners with Claims, Reserving, Actuarial, Finance, Underwriting, and Transformation teams to translate business priorities into data products, dashboards, predictive insights, automation, and AI capabilities. Manages and develops an analytics team, ensures measurable business value, promotes responsible AI adoption, and maintains data, AI, and information governance standards.

This internship develops quantitative analytics and financial engineering solutions for BlackRock’s Aladdin platform. Responsibilities include building financial risk models, applying quantitative techniques to investment and client-business problems, developing Python-based solutions, delivering actionable analytics, and collaborating with investment and business teams. Interns gain experience in financial markets, investment products, quantitative research, technology, and the Aladdin platform.

Participate in BlackRock’s quantitative investing internship across quantitative research, portfolio management, product strategy, and investment technology. Apply statistical models, machine learning, Python programming, and large-scale data analysis to investment strategies across asset classes. Learn about capital markets, portfolio trading, economic theory, client objectives, and investment product development while collaborating with research, technology, strategy, and investment professionals.

Develop and optimize Pacsun’s enterprise MicroStrategy environment, including schemas, semantic layers, dashboards, dossiers, reports, and performance tuning. Integrate generative AI, natural-language queries, predictive analytics, anomaly detection, and RAG-enabled use cases. Write complex Snowflake SQL, support ETL/ELT processes, enforce governance and security, troubleshoot platform issues, document solutions, collaborate with business stakeholders, and mentor junior developers.

Analyze large and complex datasets to identify trends, clean and transform data, query databases with SQL, and develop dashboards and visualizations using Databricks, Tableau, Power BI, and Excel. Produce analytical reports and recommendations, apply predictive analytics when appropriate, and support agile delivery through user story documentation and testing.

The Senior Analytics Engineer builds and maintains data models, ETL pipelines, analytics products, business intelligence solutions, and visualizations. The role partners with stakeholders, Data Governance, Data Science, and Data Platform teams to define metrics, improve data quality, support AI and machine learning initiatives, and establish software engineering practices. Additional responsibilities include DataOps, workflow orchestration, technical documentation, business advising, and emergency response support. The position requires advanced SQL and Python skills, data engineering experience, and travel up to 10%.

Own end-to-end analytics for a live-service game, including SQL analysis, A/B testing, metric reconciliation, dashboards, pipelines, simulations, forecasting, and applied ML. Translate findings into product, monetization, live-ops, and economy decisions. Use AI coding agents while validating their outputs, build reliable internal tools and services, maintain reporting, and communicate practical recommendations to technical and nontechnical stakeholders.

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.

Leads data analytics projects that generate insights for strategic decision-making. Develops visualization solutions with Power BI and Tableau, builds data pipelines, applies statistical modeling and predictive analytics, and manages data integrity and security. Collaborates with clients on tailored data solutions while coaching analytical teams, managing accounts, overseeing project success, and identifying business opportunities.

Leads long-range workforce and resource planning for contact center operations. Owns demand forecasting, staffing, capacity, headcount, cross-skill, and scenario models across business lines and channels. Develops advanced predictive and prescriptive analytics, performance metrics, and decision-support tools using complex data sets. Advises executives on resource allocation, service levels, costs, and business performance while governing data quality and analytical processes. Leads capacity planning analysts and drives continuous improvement through automation, machine learning, and improved forecasting methodologies.

Support analytical projects across Gartner's BTI and Product teams: analyze large datasets, build dashboards and reports, apply ML/AI and LLM tools, assist data pipeline setup, collaborate with data engineering and IT, and communicate insights to stakeholders. High-performing interns may receive full-time offers.

Entry-level Associate Data Analyst supporting analytics, data science, data engineering, and project management rotations. Deliver insights, build dashboards, automate reporting, partner with stakeholders, and apply ML/LLM tools to drive product and client analytics.

Build governed data models, tested analytics transformation layers, ETL pipelines, semantic datasets, and AI-ready data workflows. Partner with analysts, developers, and domain experts to define metrics, schemas, documentation, lineage, and validation frameworks. Support ML features, retrieval pipelines, unstructured data, and LLM output benchmarking while applying software engineering, compliance, and performance standards.

Leads global customer experience analytics strategy, developing analytical frameworks, predictive models, KPI systems, dashboards, forecasting, segmentation, retention analysis, and operational insights. Partners with CX, contact center, product, data, and business leaders to improve satisfaction, efficiency, retention, and business outcomes. Manages analytics initiatives and teams while translating complex data into executive-level recommendations and narratives.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Analyze complex datasets, build Power BI dashboards and reports, write SQL, and use Python with Pandas and NumPy for data manipulation and automation. Work extensively with Snowflake, validate and reconcile data, define KPIs, support forecasting and AI-driven analytics, and translate stakeholder requirements into analytical solutions. Present insights and recommendations while collaborating with business, data engineering, and data science teams to improve reporting, data quality, governance, and self-service BI.

Analyze payment accounts and transactions to identify fraud and credit risk, develop scalable risk strategies, and design solutions including ML features and manual review workflows. Collaborate with Engineering, Product, Data Science, and Operations to mitigate fraud across geographies, payment methods, and products. Educate Stripe users about business risks and prevention strategies.

Design and build maintainable data pipelines, ETL processes, and data architecture standards. Collaborate with product managers, analysts, and machine learning engineers to translate requirements into automated, scalable solutions. Apply software engineering best practices, asynchronous systems, APIs, streaming, testing, monitoring, and Python and SQL expertise. Help prioritize data initiatives based on business impact and communicate complex technical concepts to technical and non-technical stakeholders.

Leads advanced analytics strategy and delivery, including data preparation, statistical and machine learning model development, experimentation, validation, pricing analysis, and presentation of actionable insights. Partners with business stakeholders to identify opportunities, ensures analytical quality and automation, and builds and manages a high-performing analytics team. Requires leadership experience, advanced quantitative methods, large-scale database work, and proficiency with SQL, SAS, R, Python, and related tools.

Leads the New York analytics function and team while shaping company-wide measurement strategy. Oversees reporting standardization, cross-account measurement infrastructure, analytics capability development, experimentation, dashboarding, partner relationships, and client-facing insights. Drives analytics integration into business growth opportunities, develops team talent, and presents complex measurement recommendations to executive stakeholders. Requires extensive media analytics leadership and expertise in advanced measurement methodologies, databases, advertising channels, research partners, and verification tools.

Analyze, clean, validate, and visualize structured and unstructured data supporting military capability modernization. Develop data methodologies and pipelines, assess data quality, identify trends, establish metrics and KPIs, and present findings to clients and senior Joint Force stakeholders. Use Python, R, SQL, Palantir Foundry or Maven Smart Systems, Microsoft tools, Tableau, Qlik, and Power BI to produce insights, recommendations, documentation, and cross-functional solutions.

Own and build the company’s data layer, including metadata-driven schemas, semantic modeling, versioned query interfaces, data lineage, and auditable change tracking. Write production code, establish engineering and AI-first practices, coach engineers, and collaborate with executive and engineering leadership on architectural direction. Experience with multi-tenant SaaS and clinical data standards is advantageous.

Develops cloud-based data solutions for precision marketing to healthcare professionals in a commercial biopharma environment. The role integrates, cleanses, structures, analyzes, reconciles, and visualizes internal and external datasets, while operationalizing data science models. It collaborates with brand representatives, marketers, data scientists, and omnichannel strategists to define requirements and deliver reliable data products for business stakeholders and senior management.

Design, build, deploy, and maintain scalable cloud-native data platforms, pipelines, lakes, warehouses, and analytics solutions. Support data science, AI/ML, and business intelligence initiatives while processing structured and unstructured data. Lead collaborative teams, guide analysts and developers, assess cloud architectures, and support migration and modernization efforts for mission-driven clients.