Top Principal Data Scientist Jobs in NYC, NY

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.

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.

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 design and evolution of large-scale nodal power market models. Define mathematical approaches, develop optimization/forecasting methodologies (MILP, SCUC/SCED), collaborate with engineers to productionize models, work with product and customers to translate market problems into software, mentor data scientists, and guide long-term product strategy.

Lead design, training, evaluation, validation, and deployment of large-scale recommendation and personalization ML systems. Partner with cross-functional teams to deliver customer-focused products, apply research (RL, Transformers, causal inference), and leverage big-data tooling to turn insights into production 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.

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.

Lead strategy and technical direction for generative and multimodal vision systems. Build end-to-end model workflows, improve realism, controllability, and robustness, drive evaluation and production-readiness, partner with engineering to deploy optimized inference, and mentor scientists while owning a major technical pillar from research to production.

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.

As a Principal Associate Data Scientist at Capital One, you will develop generative AI-based solutions, collaborate with cross-functional teams, and build machine learning models to derive insights from large data sets.

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

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.

Lead data science strategy across complex customer engagements, turn ambiguous business problems into repeatable ML solutions, define modeling and evaluation standards, drive data readiness and MLOps practices, mentor teams, produce reusable IP, and represent Microsoft in executive and thought-leadership forums.

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.

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.

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

Lead Data Scientist responsible for managing projects in data engineering, model design, and infrastructure deployment while collaborating with clients to develop data-driven 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 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.