Top Remote Data & Analytics Jobs

As a Senior Data Scientist at Yahoo Mail, you will enhance user growth strategies via product innovation, overseeing research projects and analytics.

Designs and operates scalable serverless AWS data pipelines, data models, ETL/ELT workflows, feature stores, and analytics-ready data assets. Partners with AI, Analytics, Product, and Underwriting teams to support reproducible ML training and scoring, MLOps, data quality, monitoring, governance, and production troubleshooting. The role handles sensitive healthcare and financial data, optimizes serverless workloads, supports batch and near-real-time processing, and applies infrastructure-as-code, CI/CD, documentation, and responsible data-use practices.

Architects and scales cloud-native analytics platforms using Databricks and Snowflake. Builds automated pipelines, data models, decision engines, and AI enablement layers that integrate POS, syndicated, and internal data. Develops campaign measurement algorithms, productionizes analytical models, optimizes data warehouse ecosystems, and mentors data engineers. Translates business requirements into scalable SQL and Python solutions while communicating technical capabilities to business stakeholders.

Lead real-world data analytics support for life sciences and technology clients in a post-sale capacity. Serve as the primary RWD subject matter expert, answering data questions, querying healthcare datasets with SQL or Python, guiding research protocols, delivering training, developing documentation, resolving issues, and identifying emerging client needs. Build trusted stakeholder relationships, collaborate with sales and delivery teams, lead data solutioning projects, and mentor junior analysts.

Leads specialty exposure management for non-natural catastrophe and liability portfolios across insurance lines. Oversees analysts performing pre- and post-bind modeling, portfolio production, reporting, regulatory submissions, data quality, and model validation. Partners with underwriting teams to optimize risk-adjusted capacity deployment, presents portfolio insights to senior stakeholders, manages outsourcing relationships, and advances exposure management tools, workflows, AI, automation, and analytics capabilities. Requires extensive insurance or reinsurance catastrophe modeling experience and strong leadership, risk governance, SQL, and analytical skills.

Leads enterprise data engineering by architecting Snowflake and dbt platforms, scalable pipelines, data models, governance, observability, and integrations. Partners with business, architecture, compliance, and technology teams to deliver reliable data products. Resolves complex production issues, improves data quality and performance, supports AI/ML data infrastructure, and mentors engineers while establishing engineering standards and technical direction.

Leads WWT’s Data Reliability and AI Readiness pillar, setting enterprise AI data strategy and standards for trustworthy, reusable data. Oversees certification, lineage, quality SLAs, readiness scoring, data products, dashboards, and AI-powered reporting. Coordinates IT, AI engineering, analytics, business, and data teams on enterprise initiatives. Manages a Product Owner and engineers, improves operational data, promotes certified data products, and translates internal AI and data capabilities into customer-facing value.

Leads retailer-level benchmark development, external data integration, calibration, data quality validation, and trend research for panel data products. Partners with Data Science, Product, Engineering, Operations, and Client teams to investigate discrepancies, establish performance metrics, build dashboards, improve processes, and communicate actionable insights. Requires advanced SQL, Python, statistical modeling, consumer or panel data expertise, and cross-functional leadership.

Establishes and maintains enterprise data governance and stewardship frameworks, policies, controls, ownership models, quality standards, lineage, catalogs, metadata, and lifecycle practices. Partners with Security, Legal, Compliance, Technology, analytics, AI, and business stakeholders to support privacy, responsible data use, regulatory adherence, and client commitments. Monitors governance adoption, identifies risks and quality issues, leads remediation, develops reporting and training, and serves as a cross-functional data governance subject matter expert.

Perform quantitative, qualitative, and NLP analyses on unclassified, PII-free eVetting data to identify bottlenecks, recommend business-rule changes, develop predictive/prescriptive models, perform trend and network analysis, and communicate findings to stakeholders.