Top Data Science Jobs in NYC, NY

Design, run, and analyze experiments (A/B/n) to optimize personalization, retention, and loyalty for Sportsbook marketing. Partner with marketing, ML engineering, and data teams to advance experimentation tools, automate analyses, and present actionable insights to stakeholders to inform promotional strategy.

The Senior Data Scientist will leverage data insights to drive growth in lending, foster AI integration, and mentor colleagues while ensuring high-quality data reporting and visualization.

As a Data Scientist, you will collaborate with scientists to develop algorithms and software solutions for data analysis and machine learning, while engaging in multidisciplinary teamwork to meet project objectives.

As a Data Scientist in Marketing, you will enhance AI-driven marketing strategies, collaborate with teams to develop metrics, analyze marketing data, and recommend improvements for user engagement.

Perform analytics and research on bike share and shared micromobility programs, build predictive models, create dashboards (Power BI), prepare reports, monitor data quality and vendor compliance, document data procedures, maintain internal databases, and partner with leadership and operations to inform planning and policy.

Senior IC for GTM analytics who builds and owns GTM data models, pipelines, and a semantic layer; runs deep analyses on funnel, conversion, forecasting, quota, and retention; partners with product data and engineering to ensure consistent data and operationalized metrics for non-technical stakeholders.

The Senior Data Scientist will build models and analyses, design experiments, integrate datasets, and leverage AI for improved workflows and insights in data science.

The role involves building predictive cohort models, analyzing data to drive business insight, collaborating with teams, and mentoring junior analysts.

The Data Scientist will develop models and algorithms for data insights, design analytics platforms, automate pipelines, and collaborate with stakeholders to create scalable data solutions.

The role involves analyzing complex data related to private markets, developing data models, creating visualizations, and collaborating on data-driven products.

Build and deploy credit risk models for acquisition and account management. Create datasets and features from traditional and non-traditional sources, collaborate with credit, product, and engineering partners, run experiments, and monitor models in production to enable responsible lending decisions.

Design and develop data solutions and ETL pipelines, extract and profile large healthcare and pharmacy datasets, build predictive/statistical models (e.g., pricing, risk scores), and deploy insights across channels using Python, CI/CD, Git, SQL, and Tableau to inform business decisions.

The Data Scientist will build AI-driven fraud and risk systems, work with real-world data, and leverage graph techniques to improve detection and labeling. They will partner with engineering to scale data systems in the compliance domain.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Develop and deploy predictive models and statistical analyses using large healthcare and retail data sources. Build ETL pipelines, feature engineering, and distributed model training; apply ML and NLP techniques to inform solutions for pricing, costs, risk scores, and customer engagement. Support productization and deployment of data science solutions across channels.

Develop and deploy analytics applications and predictive models using large healthcare and retail datasets. Perform data mining, NLP, statistical modeling, and visualization; build production-ready data solutions and backend services; support CI/CD, model comparison, and cross-channel insight deployment to inform pharmacy, medical cost, risk scoring, and customer engagement initiatives.

Design and develop data solutions, extract and manipulate large datasets, build and deploy predictive models and ML/NLP algorithms, perform feature engineering and statistical analysis, support model deployment in cloud environments, and collaborate with UI/UX and business teams to deliver data-driven health insights.

Design, build, and deploy analytics solutions and predictive models using ML, NLP, and large healthcare datasets. Perform feature engineering, model training/tuning, MLOps, data modeling, visualization, and deliver insights to inform business and customer-health decisions across multiple channels.

Build, validate, and deploy statistical and predictive models on large healthcare datasets. Perform advanced analytics, NLP, and risk/decision analysis. Collaborate with business partners to inform network strategy, provider engagement, and consumer experience. Design data architectures, pipelines, and ML infrastructure.

Develop, validate, and deploy algorithms and predictive models using ML, NLP, and big-data tools. Create dashboards and analytics to improve patient care and healthcare operations, communicate results to stakeholders, and ensure data governance and best practices.

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.

Embedded on a core business team, define and prioritize analytical problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed product, growth, and partnership decisions end-to-end.

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

Embed in a core business area to define problems, build metrics, run analyses and experiments, surface insights, and drive outcomes by recommending and executing data-driven actions end-to-end.

Own analytics for an embedded business unit end-to-end: define problems, set metrics, build reports, run analyses and experiments, surface insights, and drive data-informed decisions and outcomes with product, growth, AI, or partnerships teams.