Top Data Science Jobs in NYC, NY

Work with media strategists, data scientists, and engineers to design and execute campaign measurement, experimentation, and optimization. Manage projects, ingest and clean data, perform statistical analyses, build reproducible Python/SQL solutions, create client-facing reports and dashboards, and help productionalize tools to improve media performance and business outcomes.

The role involves developing graph algorithms, data pipelines for identity verification, and supporting data analysis for compliance products.

Build, validate, and operationalize analytics and dashboards on Azure Databricks for Property & Casualty and Employee Benefits. Collect, cleanse, and integrate insurance data, perform exploratory and statistical analysis, prototype analytics solutions, and communicate insights via visualizations to stakeholders while collaborating with engineers and business teams.

As part of the Data Science and Analytics team, the Data Scientist will drive data-informed decision making and improve developer productivity by analyzing metrics, conducting causal analyses, and presenting actionable insights to stakeholders.

Momenti seeks a Data Scientist to analyze user gesture data and improve content interaction. Responsibilities include developing data pipelines and visualizing user behavior insights.

Summer internship in Risk Analytics supporting consulting engagements: extract, reconcile, and analyze large datasets; build repeatable data workflows; develop visualizations and statistical models; translate analyses into client-ready deliverables; use SQL, Excel, Python/R and analytics tools. Hybrid New York role with substantial travel and client-facing responsibilities.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

The Staff Data Scientist will own the analytical strategy for Firefox, driving data-informed decisions through measurement frameworks, experiments, and user growth analysis, while mentoring other data scientists.

Design and evaluate causal inference experiments and causal ML models to measure product impact, optimize resource allocation, and inform support operations. Build, deploy, and iterate statistical/ML models, analyze tradeoffs, collaborate cross-functionally, and communicate insights to influence decisions and scale data science capabilities.

Develop and apply causal inference and experimentation methods to measure and optimize marketing impact. Pull and analyze data, create features, design experiments, build end-to-end models, and collaborate with cross-functional teams. Communicate findings, lead internal white papers, and advance measurement science to inform marketing decisions.

This role involves onboarding clients, solving scientific problems, guiding data analysis on the Tempus platform, and creating documentation. Requires expertise in biomedical datasets and programming.

The Data Scientist will analyze data to enhance business performance, engage customers, and optimize product strategies through experimentation and metrics establishment.

The Data Scientist will build machine learning models to enhance claim audits, optimize performance, and improve healthcare decision-making by analyzing complex data sets.

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.

The Data Scientist will develop tools for enhancing signature management capabilities, integrate diverse data holdings, and leverage AI/ML solutions for operational support.

Customer-facing data scientist advising customers on experiment design, statistical methodology, and experimentation architecture; running and analyzing A/B tests; building scalable experimentation programs; creating repeatable frameworks and technical enablement; partnering with Sales, Solutions Engineering, Product, and Engineering to drive customer success.

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 experimentation and causal inference efforts: design, run, and analyze A/B tests; establish methodological standards; support cross-functional analytics escalations; produce research and benchmarks; enable predictive and segmentation modeling; and deliver fast, actionable analytics for product, marketing, and operations stakeholders.

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.

The Data Scientist will build predictive models, drive forecasting, design experiments, and translate findings for stakeholders, optimizing healthcare operations.

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.

Use statistical and causal inference methods to build, implement, and monitor marketing models (Bayesian MMM, multi-touch attribution), analyze A/B and uplift tests, partner with marketing to test and drive data-informed decisions from large web-product datasets.

Lead and grow a 4–6 person data science team, set the roadmap, deliver marketplace forecasting and production ML (matching, scoring), embed data science cross-functionally, and establish modeling, experimentation, and monitoring best practices.

Embed with a business unit to define problems, run analyses, design experiments, build reports, surface insights, and drive data-informed decisions across growth, product, AI, or partnerships.