Top Remote Senior Data Science Jobs in NYC, NY

Lead and build a greenfield marketing measurement function across B2C and B2B. Own attribution, incrementality, MMM, MTA, LTV, and experimentation; hire and mentor data scientists; partner with marketing leadership to turn statistical results into investment decisions; integrate measurement with data platform and GenAI tools.

Lead and grow a data science team building predictive models for credit risk and payments. Drive integration with product and engineering, run quarterly planning, mentor scientists, adopt AI-assisted and agentic workflows, and collaborate on model infrastructure and deployment.

Lead a high-impact data science team focusing on marketplace growth and analytical strategies. Manage measurement frameworks and support product decisions through data-driven insights.

Build and deploy production AI/ML systems that optimize Samsara’s global hardware supply chain. Responsibilities include demand, inventory, cost, lead-time, supplier-risk, anomaly-detection, and cash-flow modeling; real-time inference pipelines; MLOps; data infrastructure; governance; and cross-functional AI adoption. The role also sets the supply-chain AI roadmap, mentors junior scientists, establishes modeling standards, and partners with Planning, Procurement, Finance, Engineering, and Operations.

Leads fraud analytics strategy for digital payments, defining risk frameworks, fraud metrics, and analytical models. Partners with Product, Engineering, and Risk Operations to turn insights into scalable fraud mitigation solutions. Manages external data sources, defines requirements for production data pipelines, and establishes best practices for risk measurement and modeling. Serves as a senior thought leader influencing cross-functional stakeholders and executive decision-makers.

Leads GitLab’s Product Data Science team, setting its strategy, roadmap, analytical standards, and priorities. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages, hires, coaches, and retains data scientists while building a high-performing function. Provides hands-on technical review of models, analyses, and queries, and communicates insights and risks to senior leaders.

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.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.