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

Own the product modeling roadmap and end-to-end lifecycle of production machine learning models, including recommenders, routing, and agent assignment. Engineer features, design experiments, deploy iterative improvements, and define real-time model integration with engineering and product teams. Apply Bayesian methods, causal inference, and marketing measurement while translating uncertainty into business recommendations. Mentor teammates and raise data science standards through code review and documentation.

Leads development and deployment of statistical, machine learning, and causal inference models for customer decisioning. Designs experiments, estimates treatment effects and incremental value, evaluates targeting performance, and integrates analytical solutions into governed production systems. Partners with business and technology teams, communicates complex findings, and continuously improves models using experimental and operational results.

Develop and deploy statistical and machine learning models for credit risk, delinquency, defaults, loan losses, underwriting, and risk-based pricing. Analyze credit bureau, alternative, and loan performance data; build production-ready analytical pipelines; monitor model performance; and address data anomalies. Lead complex initiatives, mentor colleagues, document methodologies, and incorporate model governance, fair lending, privacy, and responsible AI considerations while partnering with Credit Risk, Product, Engineering, and Finance.

Build, train, evaluate, and deploy deep learning models for credit modeling and loss prediction. Conduct representation learning experiments, integrate models into ML pipelines, and own projects from hypothesis through measurable business outcomes. Collaborate with data scientists, engineers, and product partners, communicate findings, and develop agentic tools that accelerate data science workflows.

Develop and own advanced machine learning and AI models using bank transaction and cashflow data to enable financial product capabilities, manage risk, and drive profitable growth. Design experiments, build proofs of concept, monitor production models, and collaborate with data and machine learning engineering teams on scalability. Communicate findings and tradeoffs to business stakeholders while applying statistical, research, and financial services expertise.

Develop and refine credit risk strategies, rules, monitoring tools, and performance dashboards. Use SQL, Python, statistics, and data science to analyze complex datasets, identify risk trends, optimize processes, and influence business decisions. Collaborate with product, engineering, and operations teams to implement strategies across the customer lifecycle, while mentoring junior team members.

Own data science for customer-facing product features, including LTV prediction, experimentation, benchmarking, and forecasting. Proactively analyze data to identify customer problems and product opportunities, build and deploy production-grade models, and influence product roadmaps. Partner with Product, Engineering, and Analytics, communicate findings to varied audiences, iterate on shipped features, and lead new data-driven initiatives.

Shape company-wide machine learning strategy across credit, fraud, marketing, and growth. Build and improve consumer credit models, establish model governance and responsible modeling controls, and advance experimentation, causal inference, and business measurement. Provide technical leadership, mentor scientists, evaluate AI-assisted workflows, and partner with Legal, Compliance, Risk, Engineering, Product, and Finance to support reliable, compliant, and impactful financial decision systems.

Develop marketing effectiveness analytics using Marketing Mix Modeling, attribution, customer lifetime value, experimentation, and causal inference. Build automated, scalable model pipelines, insight-ready datasets, and self-serve dashboards. Analyze campaigns, support marketing strategy, communicate findings to executives, and lead high-impact projects independently while collaborating cross-functionally.

Lead product-focused data science and machine learning initiatives, including exploratory analysis, production model development, evaluation frameworks, generative AI, graph-based machine learning, recommendation systems, and personalization. Collaborate with engineering, product, and business teams to deliver high-impact solutions, influence strategy, and communicate insights to technical and non-technical stakeholders. The hybrid role requires four days per week in the Greater Boston area.