Top Remote Data & Analytics Jobs in NYC, NY

Provide consultative reporting and analytics for large Group Benefit Solutions clients, translate claims data into actionable insights, develop and present recommendations, build new reporting capabilities, collaborate with underwriting and account teams, troubleshoot data issues, and support operational improvement and analytics projects.

Design and build scalable data solutions for high-volume transaction streams. Partner with product and business teams to deliver analytics, dashboards, and insights. Lead data strategy, architecture, governance, quality, lineage, and access controls. Enable self-service capabilities and explore new open-source technologies to support business intelligence and operational needs.

The Data Scientist in Finance will analyze financial data, create dashboards, collaborate on data engineering, and find improvements for the business.

Serve as the primary technical escalation point for managed services data and analytics clients. Troubleshoot and enhance Power BI, Fabric, Azure, and other analytics environments; gather requirements; translate business needs into scalable solutions; support client onboarding; maintain documentation; mentor support staff; and partner with client success and consulting teams. The role also contributes to continuous improvement, training, knowledge sharing, and AI-enabled productivity initiatives.

Partner with Customer Success and CS Ops to build post-sales analytics: customer health scoring, renewal and renewal pipeline reporting, billing and support signal analysis, and expansion/upsell dashboards. Own dashboard ecosystem, translate stakeholder requests into scalable reporting, collaborate with Finance and Data Engineering, and leverage AI tools to accelerate analytic workflows and maintain data quality for retention and expansion decision-making.

Designs and maintains production data pipelines, dbt models, Airflow workflows, and Snowflake datasets integrating Workday and other People systems. Owns data quality, observability, governance, row-level security, and access controls for sensitive People data. Builds semantic models and AI agents, collaborates with analysts and stakeholders, translates requirements into reliable data products, communicates technical trade-offs, and mentors other engineers.

Leads and develops a data engineering team supporting People Analytics and AI-assisted workforce analytics. Designs scalable data models, pipelines, semantic layers, testing frameworks, and governance practices. Remains hands-on with production code, reviews, and technical problem-solving while partnering with People Analytics, business, legal, and compliance stakeholders. Establishes engineering standards across Python, SQL, dbt, Airflow, Snowflake, CI/CD, data quality, observability, privacy, and security.

Design, build, and optimize modular dbt pipelines and data models; translate ambiguous product requirements into production-grade data assets; diagnose query performance and lower warehouse compute costs (including migration to Snowflake); enforce code hygiene, data security, testing, and peer review; mentor junior engineers and adopt AI-assisted development tools to speed test generation and legacy discovery.

Analyze administrative and clinical healthcare data to build reporting solutions, identify optimization opportunities, and support clinical program effectiveness. Apply statistical analysis, data mining, and predictive modeling to improve decision-making, patient outcomes, utilization, and cost efficiency. Develop dashboards and KPIs, monitor healthcare trends, collaborate with clinical, data science, engineering, and client teams, and lead complex multidisciplinary projects.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Build and lead Ashley’s consumer insights capability, including research roadmaps, custom qualitative and quantitative studies, insights repositories, prioritization processes, and executive-ready storytelling. Translate consumer, retail, marketplace, feedback, and business data into actionable recommendations for product, marketing, merchandising, pricing, and growth decisions. Influence cross-functional stakeholders, develop AI-enabled synthesis workflows, and manage one direct report while operating resourcefully with imperfect data.