Top Remote Senior Data Science Jobs in NYC, NY

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

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.

Lead end-to-end data science projects: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, operationalize models at scale, run experiments, drive ethics/privacy, partner with customers and teams, and coach cross-disciplinary colleagues.

As a Lead Data Scientist, you'll design experiments, build predictive models, and lead a team while shaping data-driven strategies to enhance AI deployments.

Leads advanced data science for pricing, marketing incrementality, customer lifetime value, retention, churn, and commercial decision-making. Designs experimentation and causal inference methods, builds predictive models, productionizes them in Databricks, reviews external analyses, and communicates findings to executives, investors, and the board. Serves as the senior technical authority for experimentation and ensures analyses are reproducible, rigorous, and decision-focused.

Owns high-impact commercial analytics, including pricing and marketing experimentation, causal inference, LTV, CAC, retention, churn, and predictive segmentation models. Sets experimentation standards, productionizes models in Databricks, validates internal and external analyses, and translates rigorous findings for executives, boards, and investors. Partners with Finance, Marketing, Product, Revenue, and Data Engineering while establishing responsible AI-assisted analytical practices.

Own analytical relationships with travel technology partners, creating scalable reporting on supply, pricing, and performance. Conduct exploratory analysis, apply machine learning and statistical methods, build dashboards, automate recurring workflows with AI tools, and translate complex findings into recommendations for product, engineering, commercial, and executive audiences.

Build statistical and machine learning models using healthcare claims, financial, organizational, and public data. Develop entity resolution, clinical ontology, affiliation mapping, organizational inference, account segmentation, and data-sourcing methodologies. Partner with Product, Data Engineering, Engineering, and QA to move solutions from prototypes into production. Explain technical concepts clearly and work effectively amid ambiguity.