Top Senior Data Science Jobs in NYC, NY

Analyze Core customer behavior, engagement, retention, product usage, and economics to identify strategic opportunities. Translate complex analyses into executive-ready insights, presentations, and recommendations. Partner with product, marketing, finance, technology, data science, and governance teams to shape customer growth priorities. Apply AI, statistics, machine learning, SQL, and visualization techniques to solve unstructured business problems while aligning recommendations with business, risk, and compliance standards.

The Director of Data Science role at Capital One involves leading a team to build data pipelines and machine learning models, leveraging technologies like Python and AWS to derive insights for improving customer financial decisions.

Senior Data Scientist responsible for building and deploying predictive models, performing EDA and statistical analysis, designing A/B tests, and creating scalable ETL/ELT pipelines. Work includes developing production-ready ML solutions, maintaining data quality in distributed environments (Spark/Hadoop), optimizing workflows, and collaborating with product, engineering, and business teams to translate requirements into reliable, monitored solutions.

Lead data science and generative AI initiatives for Merchant Services, developing LLM-powered assistants, workflow automations, data pipelines, dashboards, and intelligent analytics solutions. Design prompts, test and monitor AI models, improve reliability, and partner with stakeholders on data models, algorithms, customer insights, and client analytics. Use Python, SQL, Snowflake, Tableau, and related tools to deliver commercially impactful solutions.

Develops end-to-end data science and generative AI solutions for payments and merchant services. Responsibilities include designing prompts, building chat assistants and workflow automations, creating Python and SQL data pipelines and Tableau dashboards, testing and monitoring AI models, developing algorithms and data models, and delivering customer insights and analytics. The role collaborates with business stakeholders and data teams to improve decision-making, automate processes, and support commercial growth.

Develop predictive models forecasting credit outcomes, apply explainability techniques to complex models, and create AI-driven decision frameworks for regulated credit decisions. Analyze large, diverse datasets and apply statistical modeling, machine learning, deep learning, classification, clustering, sentiment analysis, and time-series methods. Collaborate with machine learning engineers, product managers, credit analysts, and data engineers to deliver scalable solutions.

As a Staff Data Scientist at Gusto, you will drive data-informed decision-making, lead projects, collaborate with teams, and mentor others in statistical analysis and experimentation.

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.

Develops advanced data science, machine learning, and generative AI solutions for global payments products, especially foreign exchange and cross-border payments. Responsibilities include building analytical models and pipelines, data quality validation, anomaly detection, AI agent integration, performance monitoring, and exploratory analysis. Partners with product, engineering, sales, risk, and operations teams to translate business needs into scalable solutions, inform product strategy, improve client outcomes, and mentor junior team members.

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.

Develop and productionize AI and machine learning solutions for client, investment, and operational problems. Responsibilities include designing AI agents and agentic workflows, prototyping with foundation models and agent frameworks, building evaluation and observability systems, ensuring reliability and compliance, translating business needs into technical solutions, leading major initiatives, and presenting research and technical work to senior stakeholders.

Lead a team to design and execute analytics and test-and-learn programs for credit card products. Develop segmentation, campaign design, business case analytics, and MIS; present insights to senior stakeholders and evolve analytics tooling (Snowflake, AI-enabled apps).

Lead analytics initiatives to deliver strategic insights for consumer markets. Develop advanced analytics and BI solutions, manage teams, engage stakeholders, implement cloud analytics, and coach diverse teams to improve performance and decision-making.

As a Staff Data Scientist, you will lead complex marketplace problem-solving, architect ML systems, mentor teams, and drive data science strategy in partnership with engineering and product management.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Analyze billing patterns, healthcare claims, and complex datasets to identify anomalies, audit data integrity, and support Trust and Safety initiatives. Build risk-monitoring pipelines and compliance dashboards using SQL, Python/R, and visualization tools. Translate technical findings into risk assessments for legal, compliance, and executive partners while developing safeguards to protect patients, regulatory standing, and platform operations.

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.

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.

Lead data strategy for large 2D and 3D media datasets used in production machine learning. Coordinate with ML, annotation engineering, and technical program management teams; translate project goals into actionable data specifications; oversee dataset curation, filtering, and processing; and ensure data integrity, representativeness, and quality through analysis and inspection.

Design, build, validate, deploy, and monitor ML models (credit risk, fraud, transaction timing). Create training datasets, run A/B tests, analyze customer behavior, and collaborate with Engineering, Product, and Analytics to deliver end-to-end data science solutions.

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.

Develop, validate, deploy, and monitor AML/CTF and financial-crime models and analytics. Partner with business and engineering to translate problems into data science solutions, provide technical leadership, and support model performance, testing, and governance across enterprise initiatives.

As a Data Scientist, you will advance applied ML capabilities for healthcare, develop LLM-based chart abstraction techniques, and enhance data science best practices.

The Senior Data Scientist will lead the modernization of Impact's identity graph infrastructure by auditing and improving its construction, quality, and resolution methods, while collaborating with external partners and maintaining documentation and research.

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.