Top Data Science Jobs in NYC, NY

Embedded data scientist owning analytics end-to-end for a business unit: define problems and metrics, pull and analyze data, design experiments, build reports, surface insights, and drive recommendations that influence product, growth, and strategic decisions.

Embed within a core business area to define problems, build metrics, run analyses and experiments, create reports, surface insights, and drive data-informed product, growth, AI, or partnership outcomes end-to-end.

Embedded within a core business area, define analytics problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end with stakeholders.

Embed on a core business team to define problems, build metrics/reports, run analyses and experiments, and drive data-informed recommendations end-to-end to improve growth, product, AI/automation, or partnerships.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Lead marketing measurement strategy with MMM and MTA; design incrementality and experiment frameworks (geo experiments, lift studies); interpret modeling results for stakeholders; provide technical leadership in marketing data science and ensure privacy/HIPAA-compliant solutions for healthcare marketing.

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.

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.

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.

Lead design and delivery of people analytics products using predictive models and language models. Build unified workforce data models, semantic layers, governance, continuous listening & survey frameworks, and enforce experimental and psychometric rigor to inform talent, retention, and team-performance decisions.

Lead end-to-end experimentation and analytics for the fintech product: design A/B and multivariate tests, measure impacts on behavior, unit economics, and risk; turn model outputs into business rules; define metrics and dashboards; partner with product, finance, risk, and engineering; and communicate results to non-technical stakeholders.

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.

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.

Contribute to data pipelines, validate and refine analytical models, perform advanced analyses and data quality checks, collaborate with cross-functional teams, and write maintainable analytical code and documentation to support business decision-making.

Build full-stack data science solutions for finance: develop, test, and ship models; partner with data engineering and finance teams to create actionable financial insights; drive experimentation and analytics best practices; produce dashboards and reports to influence business decisions.

Develop and evaluate Python-based machine learning models using time-series telemetry and operational data. Responsibilities include data exploration, feature engineering, NLP, probabilistic and physics-informed modeling, anomaly detection, forecasting, validation, documentation, and production deployment. Collaborate with engineering, visualization, and program teams to integrate models into dashboards and decision-support tools. The role supports DoD customers and requires U.S. citizenship plus eligibility for a security clearance.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

The Associate Data Scientist partners with clinical and operational teams to prepare and transform data, develop predictive models, apply statistical and AI techniques, and create actionable insights. Responsibilities include feature engineering, model optimization, data visualization, dashboards, stakeholder presentations, risk management, and Agile collaboration to improve patient care, hospital operations, and organizational outcomes.

Support Legal and Finance with data analytics, ad-hoc reporting, and automation using AI tooling. Build and maintain data pipelines and self-service reporting, validate datasets, partner with engineers and stakeholders, and improve process efficiency.

Design, build, and deploy predictive models and customer segmentation to optimize Sportsbook bonus spend. Perform end-to-end projects: KPI alignment, data analysis, feature engineering, automation, visualization, and stakeholder communication. Collaborate with Data Engineering to improve pipelines and leverage AI responsibly to drive incremental revenue.

Develop and maintain predictive pricing, retention, and conversion models using GLMs and advanced analytics; build monitoring tools for rating performance; support rate filings, product pricing analyses, competitor research, and cross-functional collaboration to inform pricing and enterprise risk decisions.

The Data Scientist will analyze data for risk and trading in Daily Fantasy Sports, develop predictive models, and monitor real-time data.

Assists with analyzing complex datasets, developing predictive models for insurance underwriting, pricing, claims, and retention, and building data pipelines and automated reporting. Collaborates with actuarial, underwriting, claims, product, and data engineering teams to solve business problems. Communicates insights through visualization and data storytelling, monitors model performance and data quality, and explores new data sources and modeling techniques while supporting insurance model governance.

Analyze, clean, and validate complex healthcare data; develop statistical, machine learning, and AI models; implement agentic workflows, RAG solutions, and model fine-tuning; deploy and monitor models in production; collaborate with technical and business teams; conduct research; and document and communicate findings.

Design and build thermal performance analytics, Python-based engineering models, scalable data flows, and Edge-native applications for power plant asset performance management. Integrate historian, cloud, Edge, and industrial systems; develop automation, diagnostics, KPI validation, and root-cause workflows; support customer deployments; and lead architecture discussions while mentoring engineering teams.