Top Data Science Jobs in NYC, NY

Leads payer healthcare data science and operations consulting initiatives, using business analytics to improve payer processes and performance. Manages teams, client relationships, project resources, strategic planning, process optimization, quality standards, and operational transformation. Coaches junior staff, promotes innovative technologies, and identifies opportunities to enhance operational excellence and business outcomes.

Develop predictive models and machine-learning solutions for cybersecurity, payment fraud detection, and vulnerability analytics. Analyze large datasets using distributed processing, identify patterns, create technical rules, integrate cybersecurity and transaction data, and design performance metrics. Collaborate with product, engineering, and operations teams, translate stakeholder needs into analytical solutions, and communicate insights to leadership. The role also involves graph databases and graph machine learning, including graph neural networks.

Develop and deploy high-scale machine learning and personalization models for Capital One’s web and mobile experiences. Responsibilities include researching recommendation systems, foundation models, reinforcement learning, causal inference, and transformer architectures; analyzing large datasets; building models through training, evaluation, validation, and implementation; and collaborating with data scientists, engineers, and product managers.

Develop and deploy large-scale machine learning and personalization models for web and mobile customer experiences. Partner with data scientists, engineers, and product managers; analyze massive numeric and textual datasets; and manage model design, training, evaluation, validation, and implementation. Research advanced methods including foundation models, reinforcement learning, causal inference, transformer architectures, and recommender systems while translating technical findings into business outcomes.

Build and deploy machine learning models and scoring platforms for credit decisioning and valuation. Analyze large-scale financial and textual datasets using statistical modeling, cloud technologies, and open-source tools. Responsibilities include model design, training, validation, implementation, analytical tool development, alternative data analysis, and collaboration with software engineering and product teams to deliver scalable credit products.

Leads data science initiatives for commercial banking by developing and validating machine learning models, conducting statistical analysis and back-testing, modernizing data infrastructure, and building analytical tools for risk, underwriting, portfolio management, and client-focused products.

Build and deploy machine learning and statistical models across the credit card lifecycle, including marketing, acquisitions, underwriting, and fraud prevention. Partner with data scientists, software engineers, and product managers; analyze large-scale numeric and textual datasets; conduct model design, training, evaluation, validation, and implementation; and translate complex analyses into business outcomes.

Leads a small data science team and cross-functional initiatives transforming bank claims management through machine learning and agentic workflows. Responsibilities include data exploration, model development, evaluation, validation, deployment, roadmap development, stakeholder influence, and establishing datasets, tools, and evaluation protocols for enterprise AI. The role remains hands-on while providing strategic oversight and developing team talent.

Lead data science initiatives supporting Product and Commercial Operations. Analyze growth opportunities, define success metrics, conduct experimentation and causal inference, and deliver actionable insights. Lead initiatives such as experimentation systems and data agents while promoting strong analytical practices. Partner with stakeholders to drive measurable product impact, minimize marketplace losses, and communicate complex findings to executive audiences.

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.

Support proprietary investment research by applying data mining, statistical modeling, machine learning, and data engineering techniques to large, complex alternative datasets. Responsibilities include data collection and quality assessment, methodology selection, modeling, analysis, performance monitoring, and communicating findings to investment professionals and internal teams. The internship also involves collaborative R&D and developing compliant research products.

Build and deploy end-to-end statistical and machine learning solutions for clients and internal delivery teams. Responsibilities include scoping ambiguous problems, analyzing operational challenges, prototyping AI workflows, translating messy data and real-world constraints into scalable solutions, iterating with stakeholders, and developing reusable tooling and infrastructure. The role combines hands-on data science with technical consulting and requires communication with technical and non-technical stakeholders.

Own end-to-end product-led growth analytics, including self-serve funnel measurement, predictive modeling, experimentation, causal inference, data pipelines, and metric governance. Analyze activation, retention, monetization, churn, and usage drivers, then provide strategic recommendations to the CEO and leadership. Build and deploy production data models and machine-learning solutions while partnering across Product, Engineering, Growth, Marketing, and GTM.

Founding Data Scientist responsible for developing product analytics, earned media value metrics, attribution models, forecasting algorithms, and proprietary social commerce measurements. The role partners with engineering and product teams to improve data foundations, translate models into production code, and support public-facing data reports. Requires advanced Python, expert SQL, production software engineering skills, quantitative education, strong communication, and 4–7 years of data science experience.

Lead the end-to-end development, deployment, and maintenance of production-grade AI solutions. Design agentic AI systems with tool integrations, orchestration frameworks, and multi-step reasoning workflows. Develop LLM-based solutions using prompt engineering, evaluation, and monitoring practices. Build scalable automation applications with Python and AWS, while establishing and maintaining CI/CD pipelines for reliable deployment and continuous improvement.

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.

Leads fraud and risk-focused data science initiatives, using Python and advanced analytics to generate strategic insights and improve business performance. Manages client engagements, data pipelines, project execution, and teams while mentoring staff and resolving stakeholder issues. Responsibilities include building data architectures, developing visualizations, improving processes, and maintaining professional, technical, and independence standards.

Leads data science initiatives for US Card acquisitions, developing and deploying machine learning models across their full lifecycle. Partners with data scientists, software engineers, product managers, and business stakeholders to solve complex problems, analyze large-scale numeric and textual datasets, validate models, and translate technical findings into business outcomes. The role also involves team leadership, talent development, model governance, monitoring, and ongoing model improvements.

Leads data science initiatives for Capital One’s Card Mainstreet business. Partners with data scientists, software engineers, and product managers to develop customer-focused solutions; analyzes large-scale numeric and textual data; builds, validates, and implements machine learning models; translates technical findings into business outcomes; and supports innovation and team talent development.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

Develop and deploy deep learning models for fraud detection, risk management, and identity verification. Work across diverse data modalities, including tabular data, text, point clouds, and images, throughout the machine learning lifecycle. Responsibilities include data exploration, feature engineering, model training, evaluation, deployment, monitoring, and communicating results. Collaborate cross-functionally, make technical decisions independently, and apply emerging AI and machine learning techniques to production challenges.

Develops and deploys production machine learning models and data science solutions for business, legal, investigative, and regulatory needs. Designs AI evaluation frameworks assessing bias, accuracy, reliability, and performance; analyzes structured and unstructured data; builds pipelines and testing tools; and translates legal and business requirements into technical solutions. The role collaborates with lawyers, clients, and cross-functional teams, manages concurrent engagements, communicates insights, and supports revenue-generating professional-services delivery.

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical stakeholders.

The Product Data Scientist will establish data-driven product decision-making at Baseten. Responsibilities include defining product metrics, designing measurement plans, analyzing experiments and customer behavior, forecasting, evaluating platform reliability and releases, and building trusted reporting tools. The role partners closely with Product, Engineering, founders, and GTM to guide roadmap and prioritization decisions across a technical, usage-based AI platform.

Build ML and data systems for climate policy analysis, renewable energy optimization, and scalable climate interventions. Responsibilities include developing LLM and NLP pipelines, knowledge graphs, predictive and recommendation models, causal analyses, evaluation frameworks, data visualizations, and reproducible MLOps pipelines. The role also maintains cloud data infrastructure, APIs, model deployment workflows, monitoring, and governance while collaborating with policy experts and community stakeholders.