Top Senior Data Science Jobs in NYC, NY

Lead data science across autonomy, safety, and product by analyzing machine telemetry and operational data, defining metrics, designing experiments, and partnering with ML platform and engineering to deliver insights that improve system performance and safety.

Drive high-impact, customer-facing data science for top advertisers: advise on campaign strategy, translate account insights into product signals, build global activation frameworks, define experiments, and scale GDS playbooks across regions to influence product roadmap and advertiser outcomes.

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

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.

Build and own the analytics pipeline and semantic layer, define canonical product and business metrics, evaluate AI/agent performance, support pricing and profitability analysis, design dashboards and quality checks, translate ambiguous questions into analyses, and hire/scale the data team.

Develop and evaluate production-oriented AI capabilities for legal products. Responsibilities include designing LLM, RAG, retrieval, ranking, and agentic-system experiments; building prototypes and evaluation harnesses; analyzing errors, hallucinations, and data quality; defining benchmarks and human-in-the-loop metrics; and partnering with engineers, product teams, legal experts, and customers to improve AI workflows. The role is fully onsite in New York City with occasional customer-site travel.

Own the end-to-end data science function for a 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 leadership decisions. The role requires independently structuring ambiguous problems, using SQL or Python, communicating findings clearly, and driving measurable business outcomes.

Own end-to-end data science for a 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 leaders. The role requires independent analytical execution, first-principles problem solving, persuasive communication, and ownership of ambiguous initiatives that drive measurable business outcomes.

The data scientist will optimize credit and marketplace products using machine learning, create algorithms, analyze experiments, and manage ML infrastructure.

The Data Scientist will design and implement causal inference models for underwriting and portfolio management, create algorithms for credit pricing, and lead ML infrastructure projects.

As a Data Scientist at Anthropic, you'll support data-driven decisions across the customer lifecycle by defining metrics, analyzing data, and developing statistical models. You'll collaborate with various teams to influence strategies and improve analytics infrastructure.

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 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.

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.

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.

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.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

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.

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.

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.