Top Data & Analytics Jobs

Designs and advances the firm’s enterprise data platform, including data warehouses, lakes, semantic models, pipelines, ETL/ELT processes, dashboards, and analytics solutions. Applies statistical analysis, forecasting, predictive modeling, machine learning, and AI-ready data architecture to improve business decisions and operational efficiency. Provides technical leadership in data strategy, governance, quality, architecture, automation, and emerging analytics technologies while partnering with business and technology stakeholders.

Design and advance the firm's enterprise data and analytics capabilities. Build data platforms, warehouses, lakes, semantic models, and scalable ETL/ELT pipelines. Develop Power BI and Microsoft Fabric dashboards, KPIs, and reporting solutions. Apply statistical analysis, predictive modeling, forecasting, and machine learning to business problems. Create governed, AI-ready data assets and support intelligent automation, generative AI, and retrieval pipelines. Provide technical leadership in data architecture, governance, analytics strategy, and engineering best practices.

Designs and advances the firm's enterprise data and analytics capabilities by building data platforms, warehouses, lakes, semantic models, pipelines, dashboards, and machine learning solutions. Develops AI-ready data assets, predictive models, reporting solutions, and governed analytics capabilities. Provides technical leadership in data architecture, governance, automation, emerging technologies, and analytics strategy while partnering with business and technology stakeholders.

Designs and advances the firm's enterprise data and analytics capabilities, including data warehouses, lakes, semantic models, pipelines, dashboards, predictive models, machine learning solutions, and AI-ready data assets. The role ensures data quality, governance, scalability, and performance while partnering with business and technology leaders. Responsibilities also include translating business requirements into analytics solutions, evaluating emerging technologies, supporting AI applications, and providing technical leadership for data strategy and engineering best practices.

Designs and advances enterprise data and analytics capabilities, including data warehouses, lakes, semantic models, pipelines, dashboards, and AI-ready data assets. Applies statistical analysis, predictive modeling, forecasting, and machine learning to improve business decisions and operational efficiency. Provides technical leadership in data architecture, governance, analytics technologies, automation, and strategy while partnering with business and technology stakeholders.

Establishes and maintains enterprise data governance and stewardship frameworks, policies, controls, ownership models, quality standards, lineage, catalogs, metadata, and lifecycle practices. Partners with Security, Legal, Compliance, Technology, analytics, AI, and business stakeholders to support privacy, responsible data use, regulatory adherence, and client commitments. Monitors governance adoption, identifies risks and quality issues, leads remediation, develops reporting and training, and serves as a cross-functional data governance subject matter expert.

Build and maintain scalable data platforms and pipelines that organize structured and unstructured data. Retrieve, merge, analyze, and prepare data from APIs and databases for AI and GenAI models. Collaborate with analysts, engineers, developers, and data consumers in an agile environment while supporting data architecture, ETL, analytics, and machine learning initiatives for mission-driven clients.

Leads client-facing marketing analytics engagements, developing measurement strategies, attribution and media mix frameworks, predictive models, segmentation, forecasting, and cross-channel insights. Partners with executives, translates complex customer data into business outcomes, leads enterprise analytics initiatives, presents strategic recommendations, mentors analysts, and oversees modeling and data integration projects using modern cloud, database, visualization, and machine learning technologies.

Own supplier-side pricing, yield, monetization, retention, and churn analytics for a B2B marketplace. Build predictive models, data models, dashboards, cohort analyses, and A/B tests using SQL, Snowflake, dbt, and Looker. Partner with product, operations, engineering, sales, finance, and leadership to identify pricing and revenue opportunities, improve supplier retention, and translate marketplace insights into measurable business impact.

Own supplier-side pricing, yield, monetization, retention, and churn analytics for a B2B marketplace. Build scoring models, predictive machine learning models, dashboards, category and keyword data models, cohort analyses, and A/B tests. Partner with general management, product, operations, engineering, sales, finance, and customer success to translate marketplace insights into pricing, revenue, retention, and supplier engagement decisions.

Designs and maintains cloud-based data pipelines and architectures, primarily using Snowflake. Builds ETL/ELT processes, automates reporting, supports predictive analytics and machine learning initiatives, and helps migrate legacy platforms to the cloud. The role involves data modeling, governance, troubleshooting, AI solution development, and collaboration with analysts, data scientists, actuaries, product managers, and insurance professionals. The position is onsite four days weekly with one remote day.

Develop and maintain data pipelines, transformations, analytical workflows, dashboards, and user-facing applications using Palantir Foundry and related technologies. Analyze, clean, integrate, and validate data; develop queries and scripts; support data modeling, testing, documentation, and deployment. Collaborate with stakeholders and technical teams to translate requirements into practical solutions, troubleshoot data issues, and apply user-centered UX/UI principles. Minimum qualifications include two or more years of relevant technical experience and proficiency in Python, SQL, and JavaScript or TypeScript.

Design and build scalable analytic tools and systems to improve localization quality and efficiency. Partner with engineers, scientists, and product teams to improve data models, productize insights, and drive localization strategy. Act as an LA ambassador for the Globalization Data Science and Engineering team.

Lead a team to analyze, optimize, and grow merchant revenue using analytics and AI. Define strategy, set KPIs, deploy ML solutions, drive go-to-market alignment, execute tests, and deliver actionable insights to executives and cross-functional partners.

The Data Scientist/Manager will analyze data, develop models, support organizational missions, and provide insights to enhance decision-making for the USDA.

The Director leads global HR analytics, driving innovation and delivering data-driven strategies to enhance colleague experiences, operational excellence, and decision-making through advanced analytics and automation.

Lead the Decision Science function, designing ML models, overseeing experimentation frameworks, and ensuring model adoption across the company, driving operational and commercial improvements.

Lead automation of billing, journal entries, reconciliations, and recurring accounting reports. Design and maintain workflows connecting Accounting with other teams, analyze accounting data, build dashboards, and apply ML for forecasting and anomaly detection. Partner with IT and Accounting leadership, document controls, train staff, and drive automation projects from identification through implementation.

Partner with Supply Chain functions to design and deliver digital process improvements and analytics solutions (dashboards, automation, low-code apps, ML/AI). Own end-to-end delivery from intake, backlog prioritization, and data prep to deployment, adoption, and value realization while driving change management and reusable team practices.

TigerGraph seeks individuals to promote their scalable graph database technology used for analytics and machine learning across various industries.

Design, develop, maintain, and execute complex financial models for loss forecasting, stress testing, pricing, originations/collections, and portfolio optimization. Use Python or R (and SQL/SAS/other languages) for data processing and model development, build Power BI analytics, collaborate with business partners and regulators, support model validation/audits, research industry/academic methods, and develop data environments to support quantitative activities.

Design and implement scalable analytics/data models and dbt pipelines, manage Databricks, build dashboards and BI solutions, partner with business stakeholders, support CRM/marketing analytics, and explore GenAI/ML opportunities to deliver reliable, automated data products.

Participate in a multi-week training then rotate across analytics teams (quantitative modeling, model risk, market risk, fraud, commercial analytics, client/employee experience, credit portfolio). Perform data analysis, develop predictive models, produce reports and visualizations, communicate insights to senior stakeholders, conduct market research, and support financial and regulatory reporting.

Lead a quantitative modeling team to develop and govern credit underwriting, pricing, collections, and portfolio risk models. Collaborate with stakeholders on acquisition pricing, marketing optimization, M&A risk assessment, and model validation. Oversee model performance, governance, and implementation to support lending strategies and maximize profitability while minimizing credit losses.

Design and build data systems and analytics to turn large, structured and unstructured datasets into actionable insights. Develop and validate AI/ML models (including LLMs/agentic AI), clean and retrieve data, visualize results for non-technical leaders, and work on classified networks supporting DoW/IC clients.