Top Senior Analytics Jobs

Perform end-to-end quantitative analytics for deposit pricing, focusing on CDs and elasticity testing. Analyze large money-movement datasets to inform pricing strategy, build automated interactive dashboards, ensure data accuracy, monitor competitor and macro trends, and present insights to senior leadership while partnering with pricing, finance, risk, marketing, and product teams.

Lead end-to-end quantitative research and segmentation studies using SQL, Python, and statistical methods. Analyze large datasets and surveys, synthesize findings into executive-quality insights, drive process improvements, mentor junior staff, and partner with product and senior stakeholders to integrate research into product development.

Perform advanced quantitative analyses on large card portfolio datasets to inform marketing, product, finance, and risk decisions. Build executive dashboards and reports, apply data mining and statistical techniques, translate customer behavior into actionable insights, partner with stakeholders to optimize campaigns and portfolio profitability, and contribute to analytics architecture and process improvements with limited oversight.

Develop, validate, and govern multi-year forecasting models estimating credit card customer engagement and balances. Produce incremental impact analyses using control groups, step-up factors, and performance monitoring; provide inputs to Finance for NPV/PTI, ensure model governance, documentation, and cross-functional alignment with Risk and Finance.

Support collections and recovery by building predictive models and analytics, monitoring trends, and translating insights into operational strategies. Collaborate cross-functionally to design, test, and implement data-driven solutions, present findings using visualizations and narratives, and act as a subject-matter expert to optimize performance and manage expenses.

Lead analytics for card acquisition risk: collect and analyze internal/external data, develop strategies and controls, partner cross-functionally, present recommendations, and ensure regulatory compliance to drive financial performance and loss mitigation.

Perform quantitative analyses of business lending portfolios to generate strategy recommendations, monitor portfolio and collection performance, design and test risk/growth strategies, produce periodic reporting, support ad hoc requests, and maintain audit‑ready documentation and controls.

Design and implement forecasting and automation solutions for finance. Translate business requirements into development-ready artifacts, collaborate with stakeholders and engineering, monitor solution performance, and document progress in JIRA.

Support home lending risk strategy by performing data-driven analyses, identifying trends, monitoring emerging housing and regulatory risks, and delivering clear recommendations and visualizations. Partner with Risk Policy, Product Strategy, and cross-functional teams to design risk segmentation, controls, and performance monitoring to align growth with the firms risk appetite.

Lead pricing analytics for merchant/platform deals and international market expansion. Define pricing strategy, run economic analyses, design and test pricing constructs, and collaborate with product, partnerships, credit, finance, capital markets, and engineering to optimize portfolio performance.

Lead analytics efforts for auto pricing loss estimation: develop, validate, and implement loss assumptions and statistical models (SAS/Python) to optimize portfolio profitability, support risk and regulatory requirements, monitor performance, and translate insights into strategic pricing recommendations across cross-functional stakeholders.

Lead analytics work to develop, validate, and implement pricing loss assumptions for auto lending. Build and enhance statistical models, analyze large datasets, monitor portfolio loss performance, and provide actionable recommendations to Pricing, Risk, Finance, and senior leadership to optimize profitability and ensure regulatory compliance.

Lead analytics strategy and decision support across Product, Growth, Revenue, Operations, and Finance. Perform deep-dive analyses, design experiments, build predictive models and ML/AI prototypes, create trusted reporting and dashboards, ensure data quality, automate workflows, and partner with stakeholders to drive measurable business outcomes. Act as a primary analytics partner, prioritize work, communicate recommendations, and mentor analysts informally.

Lead quantitative analyses to design, implement, and evaluate Medicare and Medicaid payment models. Query large claims/enrollment datasets, build payment calculations, financial and quality metrics, and apply ML/econometric/predictive models. Mentor staff, write reproducible code and documentation, develop client relationships, perform rapid ad-hoc analyses, and help integrate AI into technical workflows.

Lead hypothesis-driven analyses of complex claims, payment, employee, and vendor data to detect fraud signals, generate investigator-ready leads, improve lead lifecycle, partner with investigations and modeling teams, and deliver actionable recommendations to reduce loss.

Build and maintain an enterprise fraud repository, validate and audit fraud datasets, analyze large structured datasets to identify fraud and credit risk trends, produce analytics and reports, use SAS/SQL for extraction and predictive modeling, develop risk controls and KPIs/KRIs, and support risk-based decisioning across consumer lending products.

Lead enterprise internal fraud analytics and monitoring efforts, using SAS and SQL to analyze large datasets, design detection rules, and deliver defensible solutions. Partner with senior stakeholders, risk, audit, and control teams to identify fraud patterns, build analytic monitoring, and drive risk-reduction initiatives under high regulatory and executive scrutiny.

Lead enterprise internal fraud analytics and monitoring initiatives. Use SAS and SQL to analyze large datasets, design and govern fraud detection rules, partner with senior stakeholders, and deliver defensible, audit-ready monitoring solutions that reduce employee fraud risk.

Lead development, validation, and implementation of credit scorecards, loss forecasting, and pricing models for auto lending. Drive complex analytical initiatives, monitor portfolio performance, and translate model outputs into strategic recommendations for senior leadership while ensuring regulatory compliance and collaboration with Risk, Finance, and operations.

Lead enterprise internal fraud analytics and monitoring initiatives, designing data-driven detection rules and models. Partner with senior stakeholders, IRM, audit, and control teams to deliver defensible solutions using SAS/SQL (and Python) that reduce employee fraud risk and meet regulatory scrutiny.

Lead development, validation, and implementation of credit scorecards, loss forecasting, and pricing models for auto lending. Provide thought leadership, monitor portfolio performance, translate complex analyses into executive recommendations, and collaborate with Risk, Finance, and operations to inform origination and servicing strategies.

As a Sr Associate in Fraud Analytics, you'll analyze data, develop fraud rules, implement models, and collaborate with stakeholders to mitigate fraud losses.

Serve as technical product owner for inventory data assets, define product strategy and priorities, partner with engineering and business, integrate and analyze data across Teradata/SQL Server/Hadoop/AWS, lead exploratory analysis and predictive forecasting, present insights to executives, and promote data-driven decision-making.

Lead and scale a casino analytics team to drive data-informed strategy, experimentation, and reporting. Partner with product, data science, and operations to deliver executive insights, build analytical frameworks, and develop high-performing analysts to unlock growth and operational efficiency.

Manage execution, testing, validation, and monitoring of pricing changes in Auto financing systems (CPE and LAZER). Act as liaison between Technology, Pricing, and Finance teams, develop requirements and test plans, run simulations, document results, support system upgrades/disaster recovery, and perform ad hoc portfolio performance and exception analysis.