Top Remote Senior Data Science Jobs in NYC, NY

Lead hands-on delivery of agentic conversational AI solutions for banking IVR systems using Amazon Connect and Amazon Lex. Conduct experimentation, interaction-data analysis, model evaluation, intent and slot tuning, prompt development, and NLU performance optimization. Support LLM and GenAI capabilities, identify risks, and communicate findings and recommendations to leadership and project stakeholders.

Lead end-to-end data science and machine learning projects, from scoping ambiguous client problems through production deployment. Build and evaluate statistical and ML models, analyze large datasets, run experiments, collaborate with engineers, create reusable tooling, communicate findings to diverse stakeholders, and mentor data scientists. The role combines hands-on data science with technical consulting and requires occasional customer on-call support.

Develop and refactor Python optimization algorithms for supply chain, inventory allocation, replenishment, and pricing optimization. Build predictive models, analyze large datasets, design experiments, monitor KPIs, and provide data-driven recommendations. Collaborate with business stakeholders and data science teams to deliver analytical solutions, while coaching colleagues and applying advanced statistical and operations research methods.

Leads data science for AML watchlist screening, developing multilingual NLP, entity resolution, information extraction, matching, risk-scoring, and graph-based detection systems. Builds data quality pipelines, evaluation frameworks, threshold tuning, backtesting, and production ML capabilities. Partners with Product and Engineering to deploy research at scale, leads technical initiatives, mentors peers, and translates model performance into compliance and business outcomes.

Leads advanced media measurement and data science for healthcare HCP and DTC campaigns. Builds statistical, attribution, incrementality, and predictive models; integrates large healthcare datasets; and connects media exposure to business outcomes. Develops Snowflake datasets, Tableau and Looker dashboards, and executive presentations. Partners cross-functionally with media, data, engineering, and account teams while mentoring analysts and translating complex analyses into actionable client narratives.

Analyze modeled outputs and research data to produce automated reporting, visualizations, statistical insights, and actionable guidance for political strategists and clients. Build analytics pipelines, conduct regression-based analysis, monitor research returns, identify data quality issues, and collaborate with engineers, machine learning scientists, statisticians, and political experts.

Lead client engagements using data science, AI, advanced analytics, statistical modeling, experimentation, and marketing measurement. Manage and develop analysts and data scientists, advise quantitative researchers, pitch projects, grow client relationships, and provide thought leadership on machine learning and emerging AI methods. Translate technical work into strategic recommendations for clients and executives.

Develops statistical, machine learning, AI, and predictive solutions to solve complex business problems. Mines and analyzes large datasets, builds algorithms and automated data processes, validates models, and communicates insights to business and product leaders. Designs enterprise AI applications, including generative AI and RAG solutions, while supporting data governance, visualization, security, scalability, monitoring, and responsible AI practices.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Build and improve machine learning models that identify healthcare claims errors and fraud for audit. Responsibilities include curating labeled data, engineering features, selecting optimization metrics, measuring model and business impact, refining large-scale data pipelines, and developing modeling infrastructure and team tools. The role also involves collaborating with clients and healthcare experts to align insights with business needs.