Top Data & Analytics Jobs

Builds and maintains automated dashboards, reporting, KPIs, and analytical tools for transplant logistics. Mines and transforms large datasets using SQL and Python, applies statistical analysis, and delivers actionable insights to Product, Finance, Logistics, Clinical, and Technology teams. Supports logistics network design and evaluates big data technologies while translating business needs into scalable data solutions.

Leads enterprise sanctions-screening analytics strategy, developing KPI frameworks, governance standards, dashboards, root-cause analyses, and predictive insights. Identifies false-positive drivers, workflow bottlenecks, and automation opportunities while recommending tuning and process improvements. Partners with technology, operations, compliance, and governance stakeholders to build business cases, implement analytics-enabled automation, monitor performance, and deliver executive-ready reporting.

Conduct quantitative research for systematic FX options and volatility strategies. Develop forecasting models, trading signals, market-making approaches, and systematic hedging strategies. Own the full research lifecycle, including data analysis, feature engineering, backtesting, validation, portfolio construction, performance attribution, stress testing, and live deployment. Collaborate closely with traders and engineers, build analytical tools, monitor production performance, and communicate research findings, assumptions, and risks to senior stakeholders.

Lead operations research and data analytics supporting Department of War acquisition, sustainment, workforce, and decision-analysis initiatives. Develop analytical products, predictive models, dashboards, business intelligence solutions, reports, data frameworks, and quality documentation. Collaborate with government leaders and cross-functional stakeholders to analyze disparate datasets, streamline reporting, and provide data-driven findings and recommendations.

Leads audit analytics across the full audit lifecycle, including risk-based testing, continuous auditing, reporting, dashboards, and data visualization. Advises audit teams and leadership on healthcare data, Epic systems, AI governance, automation, and AI-enabled audit solutions. Evaluates AI risks involving data integrity, bias, explainability, controls, ethics, and compliance. Provides mentoring, supports complex healthcare audits, collaborates with data owners and IT partners, and advances analytics methodologies and standards.

Leads retrospective analysis of wearable sensor and physiological data, developing analysis tools, metadata systems, visualizations, statistical models, and machine learning workflows. Investigates data anomalies across hardware-to-cloud systems, curates large heterogeneous datasets, defines analytical requirements, and translates complex data into products and reports for technical, executive, and end-user audiences. Collaborates with engineering, product, and business stakeholders while establishing analytical standards and tooling.

Supports data collection, cleaning, transformation, analysis, visualization, and quality validation. The intern develops Power BI reports and dashboards, writes SQL queries, assists with data engineering and cloud data platform initiatives, documents data lineage and business rules, and supports analytics, automation, AI-enabled solutions, testing, and stakeholder requirements gathering in a regulated biotechnology environment.

Owns analytics and optimization strategy for advertising revenue across Zynga’s mobile game portfolio. Responsibilities include monitoring performance, diagnosing trends, designing A/B tests, identifying monetization opportunities, partnering with product, analytics, and data science teams, supporting AI and ML models, leading cross-functional initiatives, and mentoring analysts.

Build and maintain scalable data pipelines and platforms that integrate structured and unstructured data from APIs and databases. Apply statistical analysis, data modeling, forecasting, optimization, and data visualization to support mission-driven client solutions. Collaborate with analysts, engineers, developers, and stakeholders in an agile environment, independently examining data and communicating findings. The role requires Python, R, SQL, Tableau, Power BI, a bachelor’s degree, TS/SCI clearance, and up to 20% travel.

Develops analytics solutions for learning and assessment data, including ETL scripts, data models, machine learning models, statistical analysis, and Power BI dashboards. Partners with faculty to gather requirements, generate insights, analyze student learning behavior, and deliver descriptive and diagnostic analytics. Provides dashboard training, consultancy, and knowledge transfer to staff while applying generative AI and large language model techniques.

Supports DoD infectious disease surveillance through data management, analysis, visualization, and implementation. Responsibilities include standardizing public health data, automating DHIS2 data pipelines, developing Tableau dashboards and surveillance reports, defining data requirements, supporting system implementation, training external clients, troubleshooting software, and providing analytical products to DoD leaders. The role also coordinates with GEIS partners, supports RFIs and program deliverables, and requires collaboration across technical, epidemiological, and government teams.

Design, build, optimize, and maintain scalable data pipelines, APIs, and microservices for high-volume regulatory datasets. Ensure data quality through automated testing, monitoring, and validation; contribute to platform architecture; integrate with NLP/ML services and ELK; support production deployments and incidents; and collaborate with Engineering and Product teams. Mentor engineers, participate in code reviews, and provide technical guidance.

Conduct quantitative research for systematic trading by analyzing market data, formulating and testing hypotheses, developing statistical and machine learning models, backtesting strategies, studying market microstructure, and partnering with technologists to deploy production-ready research and trading systems.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Conduct quantitative research and post-trade analytics for securities lending. Develop trading signals, models, algorithms, and automated workflows; own production model monitoring and governance; apply AI and machine learning; and collaborate with traders, researchers, technologists, investors, and risk managers to deliver scalable electronic trading solutions.

Lead company-wide product analytics and AI evaluation strategy, defining success metrics, quality baselines, and analytical frameworks. Build ROI and business models connecting healthcare AI adoption to clinical outcomes, cost reduction, and value-based care. Analyze product and program impact, improve data instrumentation, and translate complex findings into executive recommendations. Partner with Product, Engineering, Customer Success, clinical leaders, and healthcare customers while influencing roadmaps, investment decisions, and analytics standards.

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.

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.

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.