Top Senior Analytics Jobs

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.

Lead quantitative analytics for Voice Product, partnering with product and strategy to define measurement, analyze multi-channel customer interactions, design experiments, build segmentations, create dashboards, ensure data quality, and deliver insights. Manage and mentor a small onshore/offshore analytics team to improve customer experience and reduce operating expenses.

Perform advanced fraud risk analyses, develop and implement point-of-sale fraud strategies (rules, cutoffs, policies), identify key metrics, collaborate with cross-functional partners, and present findings to senior management to reduce fraud losses while minimizing customer impact.

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.

Lead strategic revenue cycle analyses to diagnose patient billing and collections performance, quantify ROI and value, build executive-ready recommendations and playbooks, partner cross-functionally, and mentor teammates to drive scalable, data-driven improvements.

The Staff Data Scientist will drive data-informed monetization strategies across subscriptions, advertising, and virtual currencies, collaborating with product and engineering teams and defining success metrics.

The Sr. Data Analyst will utilize industry knowledge to analyze data, develop solutions, and support customers globally, focusing on delivering high-quality IT services.

Lead data analysis projects to help merchant partners understand Upside's impact, manage analytics relationships, and improve product strategy.

Lead a team in data analytics to identify and mitigate compliance risks in lending using statistical modeling and machine learning. Oversee fair lending reviews and collaborate with cross-functional teams to implement data-driven strategies in compliance operations.

Lead analytics for marketing and product decisions through causal inference, experimentation, and deep exploratory analysis. Design and evaluate A/B tests, build KPIs and dashboards, translate insights into recommendations for senior stakeholders, mentor analysts, and ensure data accuracy and integrity across platforms.

Lead analytics and machine-learning efforts to prevent point-of-sale fraud: analyze large datasets, design and implement fraud decisioning capabilities, partner with risk modeling and cross-functional teams, build KPI monitoring and automation, and communicate findings and project outcomes to senior leadership.