Top Remote Data Science Jobs in NYC, NY

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.

Lead development of interactive safety visualizations, dashboards, analytical applications, data marts, and automated reporting for autonomous-driving safety. Build trusted, performant data products with quality monitoring, frequent refreshes, metric definitions, lineage, and statistical interpretation. Partner with safety engineers, data engineers, program teams, and executives to translate complex safety information into clear decision-support tools and continuous improvement insights.

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-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.

Lead end-to-end data science for lending products: define measurement frameworks, analyze customer and risk data, design experiments, monitor model and policy performance, and build reusable tooling. Use AI tools and agent workflows to accelerate analysis, mentor junior scientists, set technical standards, and partner with product, engineering, and risk to drive product and business decisions.

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.

Own onboarding fraud and identity decisioning through model development, validation, monitoring, experimentation, vendor evaluation, and policy improvements. Analyze application, behavioral, device, bureau, and network signals to detect identity theft, synthetic identity, and application abuse. Partner with Fraud Operations, Product, Engineering, Compliance, and Credit Strategy to productionize controls, investigate emerging patterns, measure business impact, and reduce fraud losses while minimizing false positives and applicant friction.

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; communicate recommendations; and drive decisions and measurable outcomes. The role requires independent data work, first-principles thinking, stakeholder influence, and end-to-end ownership of ambiguous problems.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independent data work, strong analytical and communication skills, first-principles problem solving, and ownership of ambiguous initiatives through measurable business impact.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring data, solving ambiguous problems, communicating findings persuasively, and driving measurable business outcomes.

Own the analytical function for an embedded business unit across growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate insights; communicate recommendations; and drive decisions and measurable outcomes. The role requires independent data work, strong analytical thinking, persuasive communication, and ownership of ambiguous problems.

Own end-to-end data science and analytics for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions and metrics, extract and structure data, conduct analyses and experiments, build reports, communicate insights, and drive data-informed decisions with cross-functional stakeholders. The role requires strong analytical thinking, independent problem ownership, and persuasive communication.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; communicate recommendations to leadership; and drive decisions and outcomes end to end. The role requires independent data work, first-principles problem solving, strong communication, and ownership of ambiguous, high-impact initiatives.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Support data-driven decision-making by collecting, cleaning, and analyzing structured and unstructured data, performing EDA, assisting in building and validating ML and statistical models, creating visualizations and dashboards, documenting findings, and collaborating with cross-functional teams.

Design, deploy, and manage machine learning models supporting client acquisition, retention, and reactivation. Develop Next Best Action, CRM, paid media, and personalization solutions using LLMs and traditional ML. Design and interpret experiments, analyze large-scale datasets, communicate recommendations to stakeholders, and collaborate with product, design, and engineering teams on product and infrastructure roadmaps.

Lead data strategy for large 2D and 3D media datasets used in production machine learning. Coordinate with ML, annotation engineering, and technical program management teams; translate project goals into actionable data specifications; oversee dataset curation, filtering, and processing; and ensure data integrity, representativeness, and quality through analysis and inspection.

Own end-to-end data science for emerging content surfaces, including product experimentation, causal inference, exploratory analysis, metrics, dashboards, and strategic insights. Identify opportunities to grow content supply, creators, and engagement while improving discovery. Lead content data architecture and quality initiatives, develop source-of-truth assets, and apply advanced statistical methods such as quasi-experiments and network testing. Partner with Product, Engineering, Design, and Research to influence product direction and communicate findings to diverse audiences.

Build and deploy machine learning, NLP, and LLM-powered solutions at scale. Improve production model performance, reliability, and accuracy; develop datasets, metrics, tools, and experimentation frameworks; and use data to influence product strategy. Partner with Product, Engineering, and business stakeholders to drive AI innovation, while serving as a technical thought leader and mentoring other data scientists.

Own the design and build of a data-quality and assurance function for a large-scale pricing intelligence data lake. Responsibilities include architecting observability, defining quality standards, building anomaly detection and health scoring, instrumenting pipelines, answering customer data-quality questions, and tracing issues to root causes across collection, processing, and source changes.

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.