Top Data & Analytics Jobs in Des Moines, IA

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.

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 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 WWT’s Data Reliability and AI Readiness pillar, establishing enterprise AI data strategy, governance, certification, lineage, quality standards, and readiness scoring. Coordinates cross-functional data initiatives, improves operational data, owns trusted data products and AI-powered reporting, and manages a product owner and engineering team. Partners with IT, AI engineering, analytics, and business teams to deliver reliable, reusable, production-ready data while improving data economics and translating internal capabilities into customer-facing offerings.

Owns the end-to-end architecture, development, infrastructure, data pipelines, and Power BI reporting platform supporting data governance, informatics, analytics, auditing, and data quality. Leads and mentors a data engineering team, defines schemas and engineering standards, ensures security and HIPAA compliance, oversees testing and Agile delivery, manages stakeholder requirements, and remains hands-on with backend technologies, data models, Power BI, and DAX.

Design and maintain healthcare data integration pipelines, models, and validation processes for claims, billing, clinical, pharmacy, EMR, FHIR, and value-based care data. Collaborate with clients and internal analytics, product, and customer success teams to interpret data, document business rules, support implementations, and build reusable Ursa data models. Apply healthcare domain expertise, data security practices, HIPAA compliance, and appropriate judgment when using AI-assisted tools.

Senior individual contributor who designs, develops, optimizes, and maintains complex SQL data solutions and Microsoft SQL Server instances. Leads data modeling, performance tuning, schema design, and data quality efforts; partners with engineering, ETL, BI, and product teams; drives standards, architecture, governance, and compliance for scalable, high-performance data platforms.

Lead design of end-to-end analytics and data solutions, produce architecture diagrams and technical designs, guide delivery teams, run client discovery and requirements sessions, support pre-sales and proposals, build reusable frameworks, and manage and mentor a delivery team to ensure quality and alignment to architecture.

Design, build, and maintain Power BI and Tableau dashboards; analyze large, complex datasets using SQL, Python, and statistical methods; partner with data engineering and business stakeholders to ensure data quality; translate findings into clear, actionable stories and executive-level presentations; support firmwide analytics growth and automation.