Top Artificial Intelligence Data Science Jobs in New York City, NY

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 methodology and advanced analytics for AI-enabled Medical Affairs workflows. Develops and evaluates statistical, machine-learning, NLP, and AI models using healthcare and real-world data; defines validation, monitoring, and decision frameworks; partners with engineering, governance, and business teams to operationalize solutions. Provides technical leadership, mentorship, documentation standards, and strategic guidance while ensuring privacy, compliance, responsible AI use, and clear communication of analytical findings.

Paid 10-week AI and data science internship supporting data analysis, dashboards, machine learning, generative AI, LLM, and AI application projects. Interns will work with technical and business stakeholders, research emerging technologies, develop prototypes, and present insights within the asset management industry. Undergraduate and master’s tracks offer exposure to business intelligence, cloud platforms, financial technology, and production-ready AI solutions.

Lead product-focused quantitative analyses and machine learning efforts to generate actionable insights, drive product decisions, and optimize features. Collaborate with cross-functional teams to design metrics, build scalable statistical solutions, create dashboards, and communicate findings while using AI tools responsibly.

Turn data into product and business decisions through metric design, experimentation, causal analysis, data modeling, deep-dive analysis, dashboards, and data storytelling. Partner with Product, Finance, GTM, Growth, and Engineering leaders to measure impact, influence priorities, and improve company-wide data practices. Model analytical data in dbt, collaborate with data engineering, and help shape an AI-first data team.

Build and deploy data science models for demand and sales forecasting, promotion effectiveness, pricing, and retail analytics. Analyze messy datasets from sell-through, inventory, deductions, trade promotions, and supply-chain signals. Develop statistical quality checks, evaluate model performance, quantify uncertainty, and collaborate with AI/ML engineers to ship production-grade insights within the product.

Lead Vercel’s consumption forecasting strategy and end-to-end machine learning systems across infrastructure products. Develop advanced time-series, probabilistic, hierarchical, causal, and predictive models for operational and financial planning. Build scalable tooling for backtesting, monitoring, drift detection, retraining, and explainability. Partner with Finance, Infrastructure, Product, and GTM leadership on revenue planning, capacity optimization, pricing, and adoption scenarios. Set technical standards, influence organizational ML practices, and mentor senior data scientists and ML engineers.

The Advanced Data Science Manager leads a team of data scientists, develops data science strategies, manages client projects, and drives business development while providing thought leadership and mentoring.

Leads the strategy for scaling data science analyses used in pharmaceutical asset acquisition and diligence. Directs cross-functional Data Science and Engineering teams, determines which one-off analyses should become landscape analyses or durable tools, sets roadmaps, evaluates statistical and technical validity, identifies AI opportunities, and connects scientific findings to business impact, asset valuation, and sourcing decisions.

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.