Top Data & Analytics Jobs

Supports data and analytics projects by designing ETL processes, developing data pipelines, integrating and validating data, and improving data quality. Creates Power BI reports and dashboards, troubleshoots data and storage issues, and helps enhance data warehouse and analytics solutions. The intern also explores AI-enabled tools, cloud platforms, and modern data engineering practices while collaborating with business partners.

Ingest, clean, label, and analyze radar data from testing and field deployments. Evaluate system performance using statistics and data science, identify anomalies and root causes, and produce reports and visualizations for stakeholders. Maintain Python-based processing pipelines and support machine learning models for target classification while collaborating with technical and customer-facing teams.

Conduct quantitative investment research by developing stock-selection signals, alpha models, portfolio construction methodologies, and production models. Prepare data, perform statistical and econometric analysis, evaluate machine-learning techniques, monitor model performance, enhance research infrastructure, and communicate findings to investment teams and stakeholders.

Analyzes card fraud threats by gathering and interpreting transaction data, quantifying losses, evaluating fraud rules, monitoring suspicious activity, and recommending control improvements. The role produces reports and presentations, supports automation, data governance, model monitoring, quality audits, and ad hoc business analysis. It partners with fraud, data governance, and model governance teams to improve detection effectiveness and reduce financial losses.

Analyzes communication data from multiple sources to identify trends, patterns, correlations, and performance gaps. Builds data pipelines, connectors, models, dashboards, reports, visualizations, and KPI products using Microsoft Power Platform, Python, SQL, and related tools. Advises Marine Corps clients and senior leaders on insights, strategic decisions, process improvement, and communication effectiveness. Requires a bachelor's degree and Secret clearance.

Collects, processes, and analyzes data from multiple sources using R, Python, and SQL. Develops reports, visualizations, analytical methods, and data-driven insights while applying statistical and introductory machine learning techniques. Collaborates with internal and external stakeholders, documents project progress in JIRA, debugs code, clarifies requirements, and communicates findings through reports and presentations.

Lead enterprise data transformation and analytics initiatives, including data engineering, BI, machine learning, cloud architecture, predictive modeling, and marketing analytics. Manage project delivery, governance, stakeholder communication, and executive presentations while mentoring data engineers, data scientists, BI developers, and project managers. Drive innovation through generative AI, optimization, and advanced statistical methods, and support talent acquisition and team development.

Lead product growth and analytics initiatives for digital lending products. Analyze customer behavior, marketing performance, and large datasets; build dashboards using Tableau and Power BI; apply statistical analysis, machine learning, regression, predictive modeling, and A/B testing. Partner with marketing, product, credit risk, engineering, and operations teams to improve conversion, revenue, and efficiency. Mentor an analytics team and develop automated reporting and analytical frameworks.

The Data Analyst will gather data from internal and external sources, create reports for teams and clients, analyze datasets using statistical techniques, identify patterns, and communicate insights. The role also requires staying current with data analysis and business intelligence trends, managing multiple projects, and supporting analytics initiatives for healthcare and pharmaceutical marketing clients.

Evaluate enterprise data architectures, governance frameworks, integration patterns, ownership models, and data quality practices. Design target-state architectures supporting analytics and AI, define governance operating models, assess data flows, develop quality controls, and create strategic roadmaps and executive recommendations. Collaborate with business and technology stakeholders on data modernization, cloud migration, AI enablement, and enterprise data management initiatives.

Supports email and broader marketing analytics through data collection, campaign measurement, reporting, dashboards, statistical analysis, visualization, and optimization recommendations. Partners with marketing teams to translate customer and campaign data into actionable insights. Responsibilities include developing analytical models, conducting qualitative and quantitative research, documenting requirements, supporting implementation, and improving marketing processes and decision-making.

Analyze customer behavior, segmentation, clickstream data, attribution, and marketing campaign performance. Design A/B tests, apply statistical modeling using SQL, Python, and SAS, and develop automated Tableau dashboards and reporting. Provide actionable insights to improve customer acquisition, retention, engagement, and marketing ROI.

Build and maintain data engineering solutions using Python, PySpark, and big data technologies. Work with AWS services including S3, EMR, and Lambda, while supporting infrastructure through Terraform. Integrate Java, REST APIs, messaging systems, Kafka, Cassandra, Airflow, Control-M, and native schedulers. Databricks experience and familiarity with machine learning workflows are preferred.

Analyzes structured and unstructured data to identify insights, develop algorithms, create dashboards and visualizations, apply statistical and machine learning methods, integrate data systems, and advise clients on data-driven decisions. Requires R, SQL, data analytics, visualization, machine learning, a bachelor’s degree, and TS/SCI clearance with polygraph. Python, data governance, and project management are preferred.

Leads technology, data, automation, analytics, and applied AI solutions supporting investment manager research across public and private markets. Translates business requirements into scalable production systems, develops applications and dashboards, improves data quality and governance, and prototypes AI/LLM capabilities. Partners with research, investment, data, technology, operations, and vendor stakeholders while managing complex initiatives, controls, priorities, and adoption.

Build and support Python data applications, migrate systems to Azure, develop ETL pipelines, manage data lake resources, and design scalable hybrid cloud architectures. The role also involves implementing statistical and machine learning models, improving data science infrastructure, documenting solutions, and supporting analytics databases.

Leads the Category Market Insights team, developing category, consumer, competitive, and portfolio intelligence to guide commercial strategy. Synthesizes data, conducts qualitative and quantitative research, creates dashboards and research repositories, monitors competitors and trends, presents insights to executives, and translates business questions into actionable research plans. The role also develops team capabilities and applies advanced analytics, including ML and AI, to business questions.

Analytics intern supporting commercial real estate functions including analytics, FP&A, accounting, reporting, and capital markets. Responsibilities include improving processes with automation, developing KPI models and interactive dashboards, recommending machine learning applications, building predictive models, and presenting a final business case. The role is part-time, in-person, and designed for a master's student seeking practical analytics and real estate industry experience.

Leads commercial analytics and business intelligence strategy, including executive dashboards, account reporting, revenue and pricing analytics, operational KPIs, forecasting, segmentation, and predictive modeling. Partners with Sales, Finance, Marketing, Operations, and Product to improve revenue, profitability, customer retention, and marketplace performance. Presents insights to executives and customers while managing analytics teams, budgets, vendors, and BI scalability.

Principal Data Analyst responsible for investigating business challenges, managing ad hoc analytics requests, creating Tableau reports, delivering data-driven recommendations, and communicating findings to stakeholders and senior leadership. The role partners with IT and Data Engineering on data pipelines and data mart architecture, manages A/B testing projects, evaluates analytics technologies, and supports self-service reporting. Strong SQL, Tableau, data storytelling, presentation, and quantitative analysis skills are required.

Analyze structured and unstructured datasets using Python, R, and SQL; perform data cleansing, statistics, visualization, and machine learning; develop dashboards and lightweight applications; integrate data systems; guide algorithm development; and advise clients on data-driven decisions, data strategy, governance, and infrastructure.

Designs and optimizes clinical data architectures, databases, and ETL pipelines using cloud technologies. Translates business requirements into reliable data solutions, performs data quality reviews, and advises teams on data engineering best practices. Collaborates with clinicians, informaticists, engineers, and data scientists while managing concurrent projects and overseeing team tasks. Requires advanced data modeling, profiling, analysis, programming, cloud, communication, leadership, and project management capabilities.

Develops and maintains dashboards, reports, scorecards, data models, forecasting solutions, and business intelligence tools. Analyzes complex datasets to identify trends, risks, opportunities, and business drivers, providing actionable recommendations to leaders. Partners with stakeholders to translate business needs into analytical solutions, communicates findings, and improves data integrity, governance, automation, and reporting capabilities. Requires familiarity with banking operations, regulatory compliance, SQL, Excel, Power BI, Tableau, and data visualization.

Performs complex fraud analytics and financial modeling for money movement products. Uses SAS, SQL, Excel, Tableau, and databases to evaluate fraud risk, develop mitigation strategies, improve portfolio performance, and recommend policy changes. Leads reporting, strategy requirements, testing, and rollout activities, supports biometrics enhancements, and helps implement machine learning capabilities while communicating recommendations to senior management.

Analyzes marketing performance, ROI, attribution, segmentation, cohorts, and testing across channels. Builds dashboards and reports, ensures data quality and lineage, partners with engineers and vendors on marketing data pipelines, and translates findings into strategic recommendations. Uses AI tools, SQL, statistical analysis, and business intelligence platforms to support demand generation, lifecycle marketing, and executive decision-making while maintaining privacy and regulatory compliance.