Top Senior Level Fintech Data & Analytics Jobs

Lead and deliver BI initiatives for Wholesale Operations by analyzing data, building dashboards and reports, mentoring staff, and driving adoption of operational metrics (productivity, SLAs, capacity). Collaborate with stakeholders to translate complex datasets into actionable insights and improve legacy reporting and data workflows.

The Senior Commerce Data Analyst will lead data analysis and management efforts, collaborating with stakeholders to enhance marketing solutions products using commerce data.

Lead end-to-end data science initiatives: design, deploy, and monitor production ML models for credit risk, pricing, collections, and fraud. Partner cross-functionally to integrate models, set development standards, mentor team members, and maintain reliable, scalable machine learning pipelines and monitoring systems.

Lead design and implementation of scalable compliance data pipelines and models, standardize metrics and processes, build automation and self-serve reporting, and collaborate with stakeholders to enable regulatory reporting and risk mitigation.

The Cloud FinOps Senior Analyst is responsible for optimizing cloud costs through analysis, collaboration with engineering and finance teams, and development of financial models, dashboards, and reports, leveraging AI for insights and automation.

Partner with tech, operations, and workforce stakeholders to analyze workforce and operational data, build forecasts and scenario models, write and optimize SQL, use Databricks, create dashboards and reports, and deliver actionable insights to inform capacity, demand, skills, and staffing decisions.

Design, build, and maintain scalable cloud-native data pipelines and ETL/ELT solutions using Python/PySpark, Databricks, DBT and AWS/Azure services. Collaborate with architects, data scientists, and product teams to optimize data models, ensure data quality, monitor pipeline performance, and drive architectural improvements and automation.

The Sr. Associate will manage AI Risk operations, oversee request workflows, track key metrics, support governance meetings, and identify process improvements.

The Senior Fraud Compliance Analyst will investigate fraud, analyze transactions for legitimacy, manage escalations, and collaborate with law enforcement while enhancing fraud detection strategies.

The role involves developing systematic trading strategies for futures, conducting data analysis, collaborating with teams, and refining trading strategies.

Design and execute data-driven fraud prevention strategies for the credit card portfolio using Python, SQL, AWS, Tableau, and statistical/machine learning models. Perform investigations, run A/B tests, develop decisioning rules, monitor trends, and report actionable insights while collaborating cross-functionally to mitigate fraud risk.

Lead and grow a data science team to optimize lending funnels, run experimentation and statistical analyses, build reports/dashboards and data marts, partner with Product/Engineering/Finance, and translate insights to senior leadership to inform product decisions.

Design, develop, test, implement, and support cloud-based data solutions. Collaborate with Agile teams on full-stack and distributed systems, using Java/Scala/Python, RDBMS/NoSQL, data warehousing and streaming tools. Mentor engineers, perform unit tests and code reviews, and deliver performant, production-ready data platforms to support machine learning and customer-facing products.

Lead analytical work to identify business opportunities, build and test models for product, pricing, marketing, and credit risk. Partner with IT, sales, operations and finance to implement solutions, monitor performance, and drive strategic, data-driven decisions to improve profitability and customer experiences.

Lead development of Navan's internal economic models, build probabilistic forecasts, run causal inference experiments (A/B, quasi-experimental), create pricing and retention frameworks, deploy production-quality Python models into AWS/Snowflake, and advise executives on economic strategy and risk.

Lead development of econometric, causal, and probabilistic forecasting models to guide Finance, Treasury, Sales, and Pricing. Design experiments and quasi-experimental analyses to identify business levers. Deploy production-quality Python models into AWS/Snowflake infrastructure and translate findings into strategic recommendations for executive and go-to-market teams.