Top Data Science Jobs in NYC, NY

Design, build, validate, deploy, and monitor ML models (credit risk, fraud, transaction timing). Create training datasets, run A/B tests, analyze customer behavior, and collaborate with Engineering, Product, and Analytics to deliver end-to-end data science solutions.

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.

The Senior Data Scientist will lead the development of personalization algorithms, collaborate cross-functionally, build data pipelines, and optimize machine learning models.

The role involves analyzing fraud and risk data, defining KPIs, collaborating on anti-fraud strategies, and creating tools and dashboards for data accessibility at Whatnot.

The Sr. Data Scientist will architect and scale data infrastructure, build robust data pipelines, and support the development of AI agents to optimize capacity management and operational decisions.

Partner with product, engineering, design, ops, and clinical teams to define metrics and experiments. Design and analyze A/B tests and quasi-experiments, apply causal inference when needed, perform funnel/cohort analyses, build and validate predictive models, and operationalize insights with monitoring and launch criteria. Produce clear, decision-ready analyses and raise organizational standards for metrics and experimentation.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

Perform end-to-end data science for underwriting, auto-quoting, and sales analytics: detect emerging risks, quantify data blind spots, build multi-signal models and confidence scoring, measure algorithmic impact on sales, and maintain ingestion and pipeline data quality and monitoring.

Embedded on a core business team, the Associate Data Scientist defines problems, builds metrics/reports, runs analyses and experiments, surfaces insights, and drives data-informed recommendations end-to-end to impact growth, product, AI/automation, or partnerships.

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

Embedded within a business unit, the Data Scientist defines the right problems, builds metrics, analyses, reports, and experiments, and delivers data-driven recommendations and end-to-end execution to improve product, growth, AI/automation, or partnerships.

Work embedded in a business unit to define problems, run analyses, build reports and experiments, surface insights, and drive data-informed decisions end-to-end with product, growth, AI, or partnerships teams.

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

Embed within a core business area to define problems, build metrics, run analyses and experiments, surface insights, and drive data-informed recommendations end-to-end with product, growth, and operations.

Use data analytics and machine learning to detect conduct and financial crime risks. Build data pipelines, clean and transform data, develop statistical and predictive models, apply graph/network analytics, produce dashboards and MI, and collaborate with stakeholders and technology teams to improve AML and market surveillance processes.

Lead decision science initiatives to improve operational efficiency and financial outcomes. Build decision frameworks, attribution and forecasting models, deliver reproducible Python/SQL code, mentor data scientists, and partner cross-functionally to measure and scale impact.

Lead Fraud Data Scientist to define ML strategy, build and productionize fraud detection models (including lending fraud), run advanced analyses and A/B tests, mentor the team, integrate ML with rule engines, and create dashboards to monitor fraud KPIs and model performance.

Lead the fraud data science function: define ML strategy, build and productionize fraud detection models, investigate fraud patterns, run experiments, collaborate with Product/Engineering/Risk, and deliver dashboards and monitoring to minimize fraud losses while preserving customer experience.

Lead the fraud data science effort: define ML strategy, build and productionize real-time fraud detection models, investigate fraud patterns with unsupervised and graph techniques, run experiments, collaborate with product/engineering/risk, and maintain dashboards to monitor model performance and fraud KPIs.

As a Senior Data Scientist, you will develop predictive models, conduct EDA, collaborate cross-functionally, and drive data-driven decisions.

The Data Scientist II will lead RWE studies, derive insights from real-world clinical data, and contribute to methodologies using advanced statistical techniques and AI tools, collaborating closely with pharmaceutical partners.

Lead data science projects to optimize drug development, develop AI models for clinical trials, and collaborate with teams on biomedical workflows.

As a Senior Staff Data Scientist, you will lead data science initiatives, mentor teams, develop frameworks for experimentation, and collaborate across departments to drive decision-making through data insights.

As a Data Scientist, you will develop algorithms, collaborate on product requirements, and communicate data-driven insights, while enhancing product offerings and delivering actionable insights.

The Staff Data Scientist will leverage data analysis and modeling to drive business decisions and strategies through causal inference and advanced analytics across multiple teams.