Top Senior Data Science Jobs in NYC, NY

Leads the full analytics lifecycle for Spectrum Reach’s advertising sales business, including data preparation, exploratory analysis, predictive modeling, validation, productionization, forecasting, and interpretation. Develops scalable analytics products, synthesizes recommendations, presents findings to diverse audiences, manages project execution, and mentors junior data professionals. Requires advanced statistical, machine learning, programming, cloud, and big-data expertise.

Develop machine learning models and scalable data-processing systems using alternative datasets for credit decisioning. Evaluate vendors and data products, engineer features, validate and monitor models, build challenger models, and guide dataset applications. Collaborate with data scientists, software engineers, and product managers while translating complex statistical insights into business outcomes. The role uses Python, AWS, Spark, H2O, and other open-source technologies across large numeric and textual datasets.

Leads alternative data strategy for credit decisioning by evaluating vendors and datasets, engineering features, building and validating machine learning models, and developing scalable processing and monitoring systems. Partners with data scientists, software engineers, and product managers while translating complex analyses into business outcomes. The role requires expertise in large-scale data analysis, statistical modeling, machine learning, and relational databases, with hybrid office attendance.

Lead the development of deep learning and computer vision models for MRI, X-ray, CT, and PET medical imaging in clinical trials. Build image processing pipelines, analyze disease progression and treatment response, develop imaging biomarkers, integrate AI into trial workflows, support FDA-compliant endpoints, and present findings to clinical, regulatory, executive, and external stakeholders.

Embedded Data Scientist for Superhuman Mail, partnering with product, growth, engineering, and design teams to guide roadmap decisions. Responsibilities include defining product metrics, designing experiments, applying causal inference and machine learning, measuring AI feature quality, analyzing activation and retention, identifying growth and conversion drivers, and communicating insights to stakeholders. The role requires strong Python, SQL, experimentation, statistics, and cross-functional influence skills.

Analyzes complex data sets, develops Python-based data solutions and pipelines, and applies advanced analytics to support client strategy and business performance. Designs dashboards and visualizations, collaborates with cross-functional teams, manages stakeholders, improves data quality, and builds client relationships. The role also involves solving complex business challenges, identifying industry trends, and delivering high-quality work within PwC’s Risk & Regulatory practice.

Analyze complex client problems using statistical modeling, exploratory analysis, data visualization, and machine learning. Develop AI and GenAI solutions, data workflows, pipelines, models, and quality checks that generate actionable business insights. Communate recommendations through documentation and visual storytelling, manage workstreams, collaborate with clients, and mentor junior team members.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Own product analytics and data science for a business-critical product area. Develop production-ready churn, resurrection, and targeting models; define metrics; analyze experiments; apply causal inference; and deliver actionable recommendations with quantified business opportunities. Partner closely with Product, Engineering, Design, and Growth teams while contributing to experimentation methodology, standards, and tooling.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Leads data science initiatives using machine learning, statistical modeling, and large-scale data analysis. Partners with software engineers and product managers to develop customer-focused products, builds and validates models across the full development lifecycle, translates technical findings into business outcomes, evaluates emerging technologies, and develops data science talent. Requires substantial experience with programming, machine learning, relational databases, cloud computing, and people management.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Lead applied AI and data science work for recommendation and personalization systems. Partner with data scientists, engineers, and product managers to deliver customer-facing products; develop, train, evaluate, validate, and implement machine learning models; analyze large-scale numeric and textual datasets; apply statistical modeling, deep learning, causal inference, reinforcement learning, and recommender-system techniques; and communicate technical findings to business stakeholders while developing team talent.

Leads growth marketing data science by building attribution, lifetime value, acquisition cost, retention, and predictive models. Designs and analyzes experiments, causal inference studies, and cohort analyses to optimize marketing budgets and unit economics. Partners with Growth, Product, Finance, analytics, and engineering leaders to develop data pipelines, dashboards, and actionable strategies for acquisition, retention, and profit growth.

Lead a data science team delivering analytics, machine learning, and AI solutions for wealth management. Partner with product, strategy, and operations stakeholders to frame problems, define success metrics, analyze client and portfolio outcomes, operationalize experimentation, and communicate executive-ready recommendations. Coach data scientists, develop scalable analytical capabilities, and identify high-value AI and agentic use cases that improve decision quality, client outcomes, and operational efficiency.

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.

Lead development of scalable AI and machine learning solutions for customer-facing applications. Build, fine-tune, evaluate, and validate NLP models and large language models, partnering with engineering, product, and design teams to operationalize them at scale. Research emerging generative AI technologies, deliver production-ready libraries and solutions, translate technical findings into business outcomes, and provide technical leadership across cross-functional teams.

Lead development of large-scale recommender and personalization systems using transformers, LLMs, reinforcement learning, sequence modeling, and multimodal learning. Build and evaluate foundation models and low-latency production systems using customer interaction data. Collaborate with engineering and product teams, translate research into practical applications, and support innovation in generative recommendations, conversational systems, and cross-domain personalization while developing team talent.

Build enterprise Generative AI solutions for Commercial Banking using multi-agent systems, advanced RAG, context engineering, tool-calling frameworks, and machine learning. Partner with data scientists, ML engineers, and product managers to develop, evaluate, validate, fine-tune, and deploy AI pipelines over large numeric and textual datasets. Translate complex technical work into business outcomes while researching emerging AI methods.

Leads a US Card data science team developing machine learning solutions for customer behavior prediction and valuation forecasting. Partners with data scientists, engineers, product managers, and business stakeholders; builds models through design, training, validation, evaluation, and implementation; analyzes large-scale numeric and textual data; translates technical findings into business outcomes; and develops team talent.

Develop enterprise Generative AI solutions for Commercial Banking using multi-agent systems, advanced RAG, context engineering, and tool-calling frameworks. Partner with data scientists, machine learning engineers, and product managers to build, evaluate, validate, fine-tune, and deploy AI pipelines across numeric and textual data. Apply statistical modeling, machine learning, deep learning, NLP, information retrieval, and cloud technologies while translating complex technical work into business outcomes.

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.

Senior Data Scientist partnering with Product, Engineering, Design, Operations, and Clinical teams to define metrics, design experiments, apply causal inference, analyze funnels and cohorts, and build predictive models. The role translates complex data into actionable product and business recommendations, operationalizes validated insights, and establishes standards for experimentation and analytical quality across patient, payer, outcomes, and provider-focused teams.

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.