Top Remote Fintech Data Science Jobs in New York City, NY

Lead and grow a data science team building predictive models for credit risk and payments. Drive integration with product and engineering, run quarterly planning, mentor scientists, adopt AI-assisted and agentic workflows, and collaborate on model infrastructure and deployment.

Leads fraud analytics strategy for digital payments, defining risk frameworks, fraud metrics, and analytical models. Partners with Product, Engineering, and Risk Operations to turn insights into scalable fraud mitigation solutions. Manages external data sources, defines requirements for production data pipelines, and establishes best practices for risk measurement and modeling. Serves as a senior thought leader influencing cross-functional stakeholders and executive decision-makers.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Lead discovery and development of intelligent payment products using data science and machine learning. Analyze large-scale transaction data to optimize routing, interchange, acceptance, fraud risk, supplier connectivity, customer acquisition, and payment economics. Build predictive models, recommendations, optimization signals, and decisioning capabilities; validate business value through experimentation; and partner with Product and Engineering to productionize solutions. Provide technical leadership, mentor analysts, and advise senior Payments stakeholders on high-value opportunities.

Collects, cleans, analyzes, and visualizes structured and unstructured data to solve business problems and improve product performance. Builds basic machine learning models, conducts exploratory analysis, experimentation, A/B testing, and statistical validation. Collaborates with Product, Engineering, and data engineering teams on data requirements and quality, while documenting methods, findings, and model outputs.

Develop and productionize machine learning and AI systems across document intelligence, language, vision, and multimodal applications. Design datasets, models, training pipelines, and evaluation frameworks; fine-tune foundation models; run GPU experiments; and build reusable AI capabilities. Partner with engineering and product teams on deployment, monitoring, reliability, and improvement while communicating technical tradeoffs and delivering measurable business outcomes in regulated financial services.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.