Top Data & Analytics Jobs

Supports Department of Defense infectious disease surveillance through data collection, standardization, management, analysis, visualization, and integration. The role implements DHIS2 across laboratory partners, automates data pipelines, develops Tableau dashboards and surveillance reports, establishes data requirements, trains clients, troubleshoots systems, and provides analytical and programmatic support to GEIS leaders and stakeholders.

Conducts data collection, cleaning, validation, statistical analysis, predictive modeling, forecasting, and visualization. Develops reports and recommendations, collaborates with cross-functional teams, supports data governance and process improvement, and contributes to strategic initiatives. The role also provides occasional guidance and training to junior analysts while maintaining data confidentiality, quality, privacy, and compliance.

Designs, builds, and maintains data pipelines, processing systems, APIs, ETL processes, and data integration infrastructure. Implements data quality and governance practices, supports data scientists and analysts, and works with data warehouses, data lakes, cloud technologies, and large datasets. The role also requires ongoing learning about data technologies and occasional driving.

Supports enterprise data architecture initiatives involving data sources, integrations, pipelines, cloud technologies, IoT and telematics data, governance, quality, visualization, artificial intelligence, and machine learning. The intern will document data flows, develop proof-of-concepts, create architecture diagrams and dashboards, analyze data for business insights, and collaborate with architects, engineers, analysts, and stakeholders to deliver a meaningful summer project.

Owns the design, development, optimization, and lifecycle management of Tableau dashboards, data products, and procurement reporting solutions. Analyzes spend, sourcing, supplier, contract, and accounts payable data; translates findings into recommendations; supports Tableau Server governance; and enables self-service analytics. Partners with procurement and cross-functional stakeholders, presents insights to senior leaders, and evaluates AI-enabled opportunities to improve business outcomes.

Supports Global Supply Chain teams by identifying data-driven improvement opportunities and developing dashboards, reports, data models, automation, and analytics solutions. The intern will cleanse, validate, visualize, and analyze data using Excel, Power BI, Python, and SQL; evaluate AI and machine learning outputs; map processes; research responsible AI practices; and present recommendations to leadership and business stakeholders.

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.

Lead and develop a team of Data Analysts while overseeing analytics projects, reporting, data governance, and self-service BI solutions. Partner with engineers, data scientists, and business leaders to establish trusted datasets, improve data quality, communicate metrics, and advance predictive analytics and AI-enabled decision support. Coach technical and non-technical staff in statistical methods and data literacy, evaluate modern analytics technologies including Microsoft Fabric, and drive insights that improve manufacturing performance, quality, and operational efficiency.

Support the design, development, and maintenance of data pipelines, workflows, and cloud-based data platforms. Build reliable data assets through ingestion, transformation, profiling, validation, monitoring, and data modeling. Collaborate with cross-functional teams to support analytics, reporting, automation, machine learning, and AI initiatives. Interns will gain experience with Python, SQL, ETL/ELT, Snowflake, cloud technologies, GitHub, and AI tools while receiving mentorship and professional development.

Data Engineer Interns support data pipelines, workflows, and platforms that enable analytics, reporting, automation, machine learning, and AI initiatives. Responsibilities include ingesting and transforming structured and unstructured data, applying data quality and validation practices, collaborating on scalable data solutions, and using Python, SQL, ETL processes, cloud technologies, and AI tools. The role includes mentorship, Agile exposure, and hands-on experience supporting data assets for insurance business needs.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.

As a Machine Learning Engineer, you will enhance internal libraries for deep learning, collaborate with researchers, and optimize workflows for cost savings.

Leads enterprise performance and analytics strategy, including Business Intelligence, executive dashboards, KPI governance, reporting automation, data standards, and decision support. Partners with Finance, IT, and business leaders to improve forecasting, planning, operational performance, and value creation. Oversees Power BI and Microsoft Fabric initiatives, enterprise reporting architecture, mergers and acquisitions analytics, Board reporting, and self-service reporting. Supervises and develops analytics personnel while promoting enterprise-wide data-driven decision-making.

Leads advanced data analytics, statistical modeling, predictive analytics, and business intelligence for supply chain and logistics operations. Analyzes complex datasets, develops forecasting and optimization models, creates Tableau or Power BI dashboards, monitors KPIs, and translates findings into recommendations for government stakeholders. Provides technical leadership, quality oversight, and mentorship to junior analysts.

Develops and maintains insurance pricing and predictive models, analyzes premiums, losses, claims, and exposure data, evaluates profitability and emerging risks, supports rate filings and actuarial analyses, and delivers recommendations to leadership. The role also ensures data integrity, prepares financial reporting, automates workflows, and collaborates with product, actuarial, and business teams to improve pricing strategies and operational efficiency.

Designs, builds, and leads enterprise data and AI platforms across Azure and Databricks. Develops scalable batch and streaming pipelines with Azure Data Factory, Databricks, and Apache Spark; implements DevSecOps, CI/CD, infrastructure as code, security, governance, data quality, and metadata standards. Supports analytics, machine learning, and AI workloads while mentoring engineering teams and partnering with stakeholders on enterprise data outcomes.

Develop and maintain BI dashboards and ETL pipelines, transform and integrate structured and unstructured data, build analytical models and forecasts, apply machine learning and distributed computing (Databricks/Spark), create actionable visualizations (Qlik/Tableau/Power BI), and present insights to leadership while working in a cleared defense environment.

Analyze and optimize e-commerce pricing strategies using sales, demand, customer behavior, competitive pricing, AI, and experimentation. Develop pricing models, measure elasticity, evaluate revenue and margin impacts, monitor competitor prices, and build dashboards, alerts, and scenario analyses. Translate analytical findings into recommendations and partner with Ecommerce, Marketing, Product, and Finance teams to improve profitable growth.

Leads advanced people analytics using Workday as the foundation for workforce insights, planning, retention, compensation, and talent decisions. Develops analytical datasets, metrics, dashboards, predictive models, and executive reporting; establishes data definitions, governance, quality controls, and architecture standards. Partners with HR Technology, Finance, HR leaders, and business executives to translate complex workforce data into strategic recommendations. Serves as a Workday and people-data subject-matter expert, mentors analysts, and influences enterprise decisions through clear storytelling.

Leads Magellan Health’s enterprise data and analytics strategy, modernization, engineering, governance, business intelligence, and AI/ML enablement. Oversees data platforms, pipelines, quality, lineage, compliance, and reporting while partnering with clinical, operational, and financial stakeholders. Builds and develops a 30+ person organization, manages a $3M–$5M budget, oversees vendors, and presents investment cases to executive leadership. Drives transformation from a low-maturity environment to a governed, insight-driven enterprise data platform.

Develop and test mathematical models for sports betting pricing and risk management. Collect, clean, and analyze sports and betting datasets to identify trends and inform trading strategies. Use Python, C#, R, or C++ to build quantitative models and automate analysis. Collaborate with senior quants, traders, engineers, and product managers, document methodologies, and communicate findings. This hybrid role requires working two to five days weekly in the Denver office.

The Data Engineer will design and build data pipelines and platforms, mentor teams, and implement AI/ML solutions for various high-impact projects, primarily in secure environments.

Data Engineer Interns will build and support data pipelines, workflows, transformations, and quality processes for analytics, reporting, automation, and AI initiatives. They will work with structured and unstructured data, cloud platforms, databases, and engineering tools while collaborating with cross-functional teams. The internship provides mentorship, Agile team exposure, and hands-on experience applying Python, SQL, ETL, data modeling, and emerging AI technologies to insurance business needs.

Clean, standardize, enrich, and import product data into the PIM while maintaining data integrity and governance standards. Oversee bulk ingestion processes, leverage AI and automation, support data model improvements, resolve product listing issues, and contribute to data hygiene and workflow modernization. The role also involves collaborating with client-facing teams, documenting processes, proofreading product data, communicating progress, and adapting to changing priorities.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.