Top Data Science Jobs in NYC, NY

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.

Lead and manage data science teams to build ML and deep learning models, maintain data pipelines and lakes, ensure data quality, deliver actionable insights, and manage project timelines and client expectations.

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

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.

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.

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

Lead data-science work for matching features: translate product goals into data problems, design and analyze A/B tests, perform exploratory analysis and data mining, generate strategic insights, and collaborate with AI, product, design, and engineering to develop and iterate production ML models.

Develop and deploy scalable statistical and machine learning models for Global Payment Solutions. Responsibilities include framing business problems, building ML pipelines, conducting research, applying NLP, forecasting, optimization, Generative AI, and running A/B tests. The role collaborates with stakeholders to convert internal and external data into revenue-generating products and insights.

Conduct statistical analysis, media measurement, experimentation, and campaign optimization using Python, SQL, and advanced analytics. Translate client objectives into measurement strategies, manage datasets, develop reproducible analyses, create dashboards and reports, and present actionable insights. Independently manage workstreams, collaborate with data scientists and engineers, support productionization of tools, and serve as a key client contact for campaign measurement, optimization, and data infrastructure.

Analyzes structured and unstructured data, performs exploratory and statistical analysis, builds dashboards and predictive machine learning models, and communicates insights to stakeholders. The role uses SQL, Python, BI tools, cloud data platforms, distributed processing, and modern AI/GenAI technologies. Responsibilities also include data quality, governance, pipelines, model deployment and monitoring, stakeholder collaboration, and translating analytical findings into business recommendations.

Lead web and marketing analytics: define tracking specs, implement and validate scalable tracking, join web and game data, build dashboards and reports, deliver campaign-level insights, measure feature effectiveness, and partner with stakeholders to make data actionable.

Senior-level data scientist responsible for developing, maintaining, and enhancing AML/CTF risk models and GenAI-enabled analytics. Collaborates with business and engineering partners to translate business problems into scalable data science solutions, monitors model performance, and communicates insights to stakeholders. Provides technical leadership, mentors teammates, and ensures compliance with regulatory and governance requirements.

Serve as a healthcare data scientist/consultant performing statistical analysis, predictive modeling, BI/reporting, and data visualization using R/SAS/SPSS and BI tools. Work independently on client projects, maintain client relationships, and report to the Senior Consultant.

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.

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.

Own network cost unit economics by building and optimizing plan configuration models, validating them against carrier invoices and financial data, forecasting usage and spend, and supporting pricing and contract decisions. Move recommendations into automated, auditable workflows, maintain dbt and Redshift data models, investigate discrepancies, and communicate quantified business impacts.

Collects, explores, and analyzes large datasets to identify business opportunities and improve outcomes. Develops predictive models, machine-learning algorithms, testing frameworks, and data pipelines; evaluates data quality and model performance; and communicates actionable insights to stakeholders and clients. The role also manages data science projects, establishes reporting best practices, and collaborates with cross-functional teams to implement and monitor solutions.

Develop and maintain credit risk, repayment, and predictive models powering financial products. Engineer features from transaction data, deploy and monitor production ML models using AWS and Snowflake, analyze model performance and drift, prototype modeling methods, and collaborate with product, risk, and engineering teams. Communicate findings to technical and non-technical stakeholders while applying explainable AI, time-series modeling, and financial machine-learning best practices.

Build predictive and cohort models supporting underwriting and marketing. Source, evaluate, combine, and analyze internal and external data; develop analytical datasets and solutions; implement and monitor models in production; communicate insights to business partners; document methodologies and results; and support statistical analysis requests. May supervise 1–2 analysts and provide guidance on cohort modeling and related analytics techniques.

Build and maintain forecasting, driver-based models, and analytics for FP&A. Analyze historical and operational drivers, improve forecast accuracy with statistical and ML methods, create dashboards and automated reports, integrate ERP/CRM data, perform variance and scenario analysis, and present insights to finance leaders.

Design and deliver analyses, reports, and dashboards; translate technical findings for business leaders; perform complex data queries and reconciliation; identify data capabilities and partner with enablement teams; mentor analysts and drive a data-driven culture across functions.

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.

Design and develop data solutions and predictive models from large healthcare data sources. Perform ETL, statistical analysis, and machine learning to inform pharmacy, medical cost, risk scoring, and customer engagement use cases. Support deployment of insights, collaborate with teams, and use CI/CD and agile practices to deliver analytics solutions.

Embed with a business unit to define analytic problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end using SQL/Python.

Embedded in a core business area, own end-to-end analytics: define problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions to improve product, growth, AI/automation, or partnerships.