Top Data Science Jobs in NYC, NY

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.

Turn data into product and business decisions through metric design, experimentation, causal analysis, data modeling, deep-dive analysis, dashboards, and data storytelling. Partner with Product, Finance, GTM, Growth, and Engineering leaders to measure impact, influence priorities, and improve company-wide data practices. Model analytical data in dbt, collaborate with data engineering, and help shape an AI-first data team.

Own onboarding fraud and identity decisioning through model development, validation, monitoring, experimentation, vendor evaluation, and policy improvements. Analyze application, behavioral, device, bureau, and network signals to detect identity theft, synthetic identity, and application abuse. Partner with Fraud Operations, Product, Engineering, Compliance, and Credit Strategy to productionize controls, investigate emerging patterns, measure business impact, and reduce fraud losses while minimizing false positives and applicant friction.

Build AI, machine-learning, and statistical models to predict clinical-trial outcomes, regulatory results, and commercial potential. Analyze trial designs, endpoints, patient populations, treatment effects, safety, and subgroup data. Apply generative AI, LLMs, and NLP to scientific and regulatory information. Develop pipeline-monitoring tools, validate models, control bias and data leakage, and translate clinical and quantitative findings into investment conclusions supporting equity research, forecasts, valuations, and published analysis.

Develops AI and machine learning healthcare products, including NLP models for unstructured medical records and classification models for clinical programs. Responsibilities include designing, training, deploying, and evaluating ML and deep learning architectures; conducting statistical analysis, exploratory data analysis, and visualization; resolving data issues; delivering analytic insights; and supporting governance, reporting, and process improvements.

Build and deploy data science models for demand and sales forecasting, promotion effectiveness, pricing, and retail analytics. Analyze messy datasets from sell-through, inventory, deductions, trade promotions, and supply-chain signals. Develop statistical quality checks, evaluate model performance, quantify uncertainty, and collaborate with AI/ML engineers to ship production-grade insights within the product.

Develop machine learning, statistical, graph-based, and entity-resolution algorithms for large-scale identity verification and deceased monitoring. Build Spark/PySpark and AWS data pipelines, engineer features, analyze data quality, conduct experiments, and support model evaluation. Write Python/R and SQL for extraction and validation, investigate compliance data, create dashboards, and communicate findings to cross-functional teams.

Develop and improve analytical and machine learning models supporting product quality, risk outcomes, and business decisions. Responsibilities include statistical analysis, feature development, data exploration, predictive modeling, SQL-based analysis of large datasets, algorithm research, and workflow documentation. The role collaborates with data scientists, product managers, data engineers, and analysts in a product-focused fintech environment.

Build and maintain dbt and Snowflake data models supporting reporting, experimentation, and machine learning use cases. Analyze consumer payment behavior, pricing, credit, and risk dynamics; design experiments; instrument product features; and create dashboards for Product, Risk, and GTM teams. Establish data quality standards, scalable data architecture, documentation, and technical direction while mentoring analysts and engineers.

Conduct statistical analysis and develop reproducible code to inform transportation policy. Manage data pipelines, data quality, governance, documentation, dictionaries, databases, and legacy-to-cloud systems. Lead complex data projects, respond to analytical requests, present findings to senior leaders and external stakeholders, collaborate across technical and non-technical teams, and mentor colleagues. Projects address congestion, fares, driver pay, vehicle electrification, and charging infrastructure.

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; communicate recommendations; and drive decisions and measurable outcomes. The role requires independent data work, first-principles thinking, stakeholder influence, and end-to-end ownership of ambiguous problems.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independent data work, strong analytical and communication skills, first-principles problem solving, and ownership of ambiguous initiatives through measurable business impact.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence product, growth, finance, and operations decisions. The role requires independently structuring data, solving ambiguous problems, communicating findings persuasively, and driving measurable business outcomes.

Own end-to-end data science and analytics for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions and metrics, extract and structure data, conduct analyses and experiments, build reports, communicate insights, and drive data-informed decisions with cross-functional stakeholders. The role requires strong analytical thinking, independent problem ownership, and persuasive communication.

Own the analytical function for an embedded business unit across growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate insights; communicate recommendations; and drive decisions and measurable outcomes. The role requires independent data work, strong analytical thinking, persuasive communication, and ownership of ambiguous problems.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; communicate recommendations to leadership; and drive decisions and outcomes end to end. The role requires independent data work, first-principles problem solving, strong communication, and ownership of ambiguous, high-impact initiatives.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Lead data science and analytics initiatives supporting product, engineering, leadership, and insurance clients. Analyze claims and product data, design experiments, generate hypotheses, measure ROI, build dashboards and reproducible analytics, and communicate actionable recommendations. Mentor junior analysts, conduct client-facing analyses, collaborate with ML teams, investigate data issues, and shape product strategy through sophisticated statistical modeling and exploratory analysis.

Lead product measurement strategy and complex evaluation for major product areas. Own experimentation standards, design causal and Bayesian analyses, build scalable measurement systems and dashboards, drive cross-team initiatives, mentor data scientists, and influence product decisions to improve mental healthcare outcomes.

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.

Build core dbt and Snowflake data models, analyze consumer payments, credit, pricing, and risk behavior, design experiments, create dashboards, improve data quality, and set technical direction while mentoring analysts and engineers.

Designs, prototypes, evaluates, and improves LLM-powered legal AI systems and agentic workflows. Responsibilities include prompt engineering, RAG, retrieval and ranking, model experimentation, evaluation benchmarks, error analysis, monitoring, and production-oriented coding. The role engages lawyers and enterprise stakeholders to define measurable problems, validate AI experiences, and establish quality standards for accuracy, grounding, hallucination detection, tool use, reliability, cost, and latency.

Support data-driven decision-making by collecting, cleaning, and analyzing structured and unstructured data, performing EDA, assisting in building and validating ML and statistical models, creating visualizations and dashboards, documenting findings, and collaborating with cross-functional teams.