Top Principal Data Scientist Jobs in NYC, NY

Lead research, design, and evaluation of large-scale detection models for automated, fraudulent, and malicious activity. Define measurement frameworks for noisy or incomplete ground truth, mentor data science practitioners, advance AI/ML techniques, and shape long-term detection strategy. Partner with ML Engineering, Data Engineering, and Product teams to influence roadmaps and protect Cloudflare customers from evolving threats.

Lead the integration of advanced imaging analytics and AI in drug development, focusing on medical image analysis and clinical trials.

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

Develop generative AI and machine learning solutions for customer-facing and internal applications. Responsibilities include partnering with data scientists, engineers, and product managers; analyzing large numeric and textual datasets; building, training, validating, evaluating, and implementing models; researching emerging AI technologies; and translating technical findings into business outcomes.

Lead a team of data scientists to develop AI/ML solutions, leveraging advanced technologies for customer interactions and product innovations. Build and implement NLP models for large-scale applications while collaborating with cross-functional teams.

Develop machine learning models supporting credit underwriting and business card strategies. Partner with data scientists, software engineers, product managers, and business stakeholders to design, train, validate, evaluate, backtest, and implement models. Analyze large numeric and textual datasets using Python, AWS, Spark, H2O, and related technologies, translating complex findings into business outcomes.

Lead high-impact modeling initiatives using deep learning, LLMs, and traditional methods. Prototype, deploy, and monitor hybrid models on large insurance datasets, drive MLOps standards, and communicate results to nontechnical stakeholders to realize business impact.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

Lead development and deployment of machine learning models across the credit card lifecycle. Partner with cross-functional teams to build data-driven products, analyze large numeric and textual datasets, validate and implement models, and translate technical insights into business impact.

Lead end-to-end delivery of strategic data science and AI solutions for AI infrastructure. Work with telemetry and experimentation at fleet scale to build, deploy, and measure predictive and prescriptive models. Partner with stakeholders, present insights, enable adoption, and promote responsible AI. Collaborate closely with data engineers and infrastructure teams to productionize scalable ML solutions.

Lead development of next-generation entity resolution using transformers, graph ML, and deep learning at scale. Partner with engineers and product to design, train, validate, and deploy models on AWS/Spark, leverage agentic AI workflows, and translate technical work into business impact across enterprise use cases.

Lead development of next-generation entity resolution solutions using transformers, graph ML, deep learning, and big-data tooling. Partner with engineers and product to design, train, evaluate, validate, and deploy ML models at scale; leverage Agentic AI workflows; translate technical work into business impact across enterprise use cases.

Design, train, evaluate, and productionize machine learning models (including embeddings and foundation models) on large-scale data. Partner with product and engineering to run experiments, improve customer experiences, and deploy scalable solutions using Spark and AWS.

Design, build, validate, and deploy machine learning models across the credit card lifecycle. Partner with cross-functional teams, analyze large numeric and textual datasets using Python, Spark, H2O and cloud platforms, and translate technical insights into business impact for card products.

Lead scientific design and data prototyping for Tempus' multimodal clinical-genomic platform. Perform data evaluation, write technical code (SQL, R, Python), design scalable data models (TDM), validate terabyte-scale datasets across oncology, neurology, and immunology, and translate findings into production-grade data products while advising clients and mentoring cross-functional teams.

Lead end-to-end data science initiatives for commercial property or homeowners insurance: acquire and prepare data, build and validate predictive models, deploy and monitor production models, ensure governance and documentation, advise teams, and evaluate emerging ML/AI techniques to improve pricing, risk selection, and underwriting outcomes.

As a Senior Data Scientist at Yahoo Mail, you will enhance user growth strategies via product innovation, overseeing research projects and analytics.

Develop and validate SDTM and ADaM datasets using R per CDISC standards, support TLF generation in R or SAS, perform QC and reconcile data issues, collaborate with biostatistics and clinical teams, and contribute to automation and reproducible workflows for regulatory submissions.

The role involves collaborating with teams to develop AI-powered products, leveraging NLP and LLM technologies while operationalizing models into production systems.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Leads the design, development, deployment, and monitoring of advanced statistical and machine learning models for insurance pricing, underwriting, claims, fraud, retention, and growth. Advises executives and actuarial teams, establishes modeling and experimentation standards, ensures responsible and scalable productionization, and mentors data scientists. The role also advances MLOps, AI infrastructure, model lifecycle management, explainability, fairness, and data science strategy.

Lead individual-contributor data science work to build predictive models, measurement frameworks, and AI-powered analytics. Partner with Product, Engineering, and Operations to translate insights into product strategy, define success metrics, automate analyses with agentic workflows, apply causal inference and experimentation, communicate findings to executives, and mentor other data scientists.

Build and deploy AI/ML solutions from prototype through production for CMS, including generative AI, LLMs, RAG, intelligent classification, knowledge management, agentic frameworks, and enterprise integrations. Evaluate AI coding tools, establish AI/ML roadmaps, scale adoption, mentor data scientists, and translate technical concepts for stakeholders and leadership.

As the first Data Scientist at Hang, you will analyze data to drive insights, develop predictive models, collaborate with teams, visualize data, and contribute to data strategies.