Top Remote Data Science Jobs in NYC, NY

Lead product-focused data science for Cash App Lending: define measurement frameworks, run experiments, analyze customer and risk data, use AI tools to accelerate analysis, partner with product/engineering/risk, drive technical standards, and mentor junior data scientists to inform product and policy decisions.

Perform quantitative analysis, data mining, and statistical modeling to generate actionable product and business insights. Design and track core metrics, build dashboards and visuals, collaborate with cross-functional teams, use AI tools to scale analyses, and drive projects to completion to inform product decisions.

As a Product Data Scientist, you will analyze user behavior, drive experimentation, build data tools, and collaborate across teams to influence product strategy and innovation at Whatnot.

Lead commercial data science for Headway's commercial business: own roadmap, run proactive strategic analyses for executive accounts, monitor key metrics, enable self-service analytics, mentor data scientists, and translate technical findings into actionable recommendations tied to measurable outcomes.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

As a Staff Data Scientist, you will lead complex marketplace problem-solving, architect ML systems, mentor teams, and drive data science strategy in partnership with engineering and product management.

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 product-focused quantitative analyses and machine learning efforts to generate actionable insights, drive product decisions, and optimize features. Collaborate with cross-functional teams to design metrics, build scalable statistical solutions, create dashboards, and communicate findings while using AI tools responsibly.

Build, validate, and productionize clustering and attribution heuristics on large multi-chain on‑chain datasets. Own end-to-end data pipelines, investigate edge cases (mixers, bridges, consolidation), benchmark against ground truth, and partner with investigations and product to define and defend precision/coverage metrics.

Lead the fraud data science team to design, build, deploy, and monitor real-time fraud detection models. Perform feature engineering, unsupervised analysis, A/B testing, stakeholder collaboration, and dashboarding to reduce fraud losses and improve model performance and explainability.

Embed within a core business area to define problems, build metrics, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Embedded on a core business team, define the right problems, build metrics and reports, run analyses and experiments, surface actionable insights, and drive data-informed decisions end-to-end across growth, product, AI, or partnerships.

Embedded data scientist owning analytics for a business unit: define problems, build metrics/reports/experiments, surface insights, and drive data-informed decisions across growth, product, AI, and partnerships.

Embed with a core business area to define problems, build metrics/reports, run analyses and experiments, and drive data-informed decisions end-to-end. Translate ambiguous questions into clear hypotheses, surface insights, and partner with product, growth, finance, and ops to execute recommendations and deliver measurable business impact.

Embedded on a central Data Science team, you will define problems, build metrics/reports, run analyses and experiments, surface insights, and drive decisions end-to-end for growth, product, AI/automation, or partnerships.

Embedded on a business unit, define analytic questions, build metrics/reports, run analyses and experiments, surface insights, and drive data-driven product, growth, AI, or partnership decisions end-to-end.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Lead product-focused analytics and experimentation to improve core experience and AI initiatives. Build propensity models and segmentation, create automated dashboards and executive reports, run and monitor experiments, and partner cross-functionally to translate analysis into business impact and drive product decisions.

The Staff Data Scientist will advance machine learning models to optimize claim audits and improve business outcomes in healthcare.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

Lead the fraud data science function: define ML strategy, build and deploy real-time fraud detection models, investigate fraud patterns (including lending fraud), run experiments, collaborate with stakeholders, and maintain monitoring and dashboards to minimize fraud losses.

Lead Fraud Data Scientist to define ML strategy, build and productionize fraud detection models (supervised and unsupervised), investigate emerging fraud patterns, run experiments, collaborate with Product/Engineering/Risk, and maintain dashboards tracking fraud KPIs.

Lead the fraud data science function: define ML strategy, build and productionize fraud and lending-fraud models, run advanced analyses (clustering, network analysis), design A/B tests, integrate ML with rules engines, monitor models in cloud, and communicate results to stakeholders.

Analyze complex datasets, build and validate predictive models, create data visualizations, ensure data quality, and partner with business stakeholders to identify and drive data-driven growth opportunities while staying current with industry trends.

Perform statistical analysis, predictive modeling, and data visualization on workforce and training data. Build dashboards, produce ad hoc analyses and decision-support products, advise on data governance/privacy, and collaborate with stakeholders to define metrics, deliverables, and reproducible analytic workflows.