Top Data & Analytics Jobs

The role involves collaborating to develop and refine large language models and generative models, utilizing expertise in machine learning and NLP.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Lead fraud risk for a conversational lending product (SNPL): define first-party fraud typologies, analyze repayment and chargeback trends, own fraud KPIs, design automated/manual blocking policies, coordinate ML/rules with central Fraud and Data teams, and liaise with Compliance on SARs and loan abuse.

Design, build, and maintain large-scale data structures, pipelines, and ETL workflows; integrate data for analytics and ML; collaborate with Data Science to operationalize models; ensure data quality, performance, and scalability across cloud and big-data platforms.

Design, develop, test, and implement complex BI solutions. Gather and manage requirements, build reports, dashboards, and data models, collaborate with business and IS teams, and apply machine learning and statistical techniques to large healthcare and retail datasets to produce financial and market intelligence.

The role involves designing and developing tools in Python for trading and risk management, maintaining data pipelines, and collaborating with teams to enhance trading strategies using advanced techniques.

The Chief Data Officer will lead the enterprise-wide data strategy, ensuring data quality and governance while driving AI initiatives and business value.

As a Credit Risk Senior Analyst at Marqeta, you'll lead underwriting strategies, refine credit policies, conduct risk analyses, and present findings to senior management.

The Data & Analytics team at Surescripts transforms healthcare data into actionable insights, collaborating with various stakeholders to support critical decisions across the healthcare ecosystem.

Design, build, and maintain scalable data infrastructure and ETL/ELT pipelines in cloud environments. Collaborate with data scientists and engineers to enable ML model development and feed software products. Translate stakeholder requirements, manage prototype-to-production transitions, and recommend new data technologies. Provide ad hoc engineering, POC development, and data analysis support.

This role focuses on sourcing, due diligence, and portfolio support in the AI investment ecosystem. It requires strong networking and analytical skills, combined with a proactive entrepreneurial spirit.

Lead a team of data scientists to enhance patient experiences through data insights, predictive modeling, and analytics for healthcare solutions.

Lead client-facing CRM analytics and insights across segmentation, campaign measurement, predictive modeling, and AI-driven marketing. Translate goals into metrics and analytical plans, manage end-to-end delivery, ensure data pipeline integrity with engineering partners, and mentor junior analysts while driving scalable, repeatable analytics solutions and dashboards.

Own and build the end-to-end data layer: extraction pipelines from messy supplier emails and PDFs, the unified data model, reliable observable pipelines, and ML-driven extraction evaluation. Drive extraction accuracy, data quality, lineage, monitoring, and automation to scale coverage and enable engineering and product to depend on the data layer.

Designs and delivers complex data solutions and pipelines for operational and analytical systems. Builds, tunes, and integrates Hadoop ecosystem components; develops containerized applications, deploys scalable ML models, and contributes to testing and performance analysis. Collaborates with stakeholders, product owners, and engineering teams to implement data requirements and troubleshoot issues.

Lead payments analytics by building scalable reporting, causal inference and ML models, designing experiments, partnering with product/engineering/finance to optimize payment processing, reconciliation, and settlement, and translating results into executive recommendations to drive global payments improvements.

Partner with investment professionals to design, build, and maintain data pipelines, Azure SQL environments, dashboards, Streamlit and LLM-based analytics tools, and vendor integrations to improve investment decision-making, data governance, and reporting across multi-asset portfolios.

Design and manage SQL Server Always-On databases and schemas, build stored-procedure DB interfaces to ATE and visualization/analysis tools, integrate test data with other sources, and implement statistical and machine-learning data characterization. Manage DB source code and validation test plans while collaborating with physicists, engineers, and test teams.

Lead enterprise data and analytics strategy for UMass Memorial Health, deliver a Unified Data Platform, establish governance and data quality, advance AI/ML and predictive analytics, modernize analytics infrastructure, build a high-performing team, and partner with clinical and operational leaders to drive measurable performance improvements and data-driven decision-making.

The role oversees analytical characterization and comparability studies for biologics, ensuring regulatory compliance and leadership in product development.

Develop methods for optimizing AI applications, implement automated evaluation systems, and work with clients to adapt tools for various domains.

The role involves conducting impactful research and teaching at the graduate and undergraduate levels in information systems and technology, focusing on analytics and AI applications.

As a Staff Data Scientist, you will lead product strategy by building predictive models, optimizing matching algorithms, and guiding product innovations in a consumer tech environment.

The Marketing Data Scientist will apply advanced analytics and machine learning to drive marketing strategies, focusing on connecting digital engagement to physical outcomes. Responsibilities include experimentation, reporting, predictive modeling, and collaboration with stakeholders for business insights.

The Forecasting Manager is responsible for demand forecasting accuracy, managing forecasts using machine learning, and training marketing & sales teams. Collaborates with top management to ensure forecast consistency and leads process optimization projects.