Top Data & Analytics Jobs in Seattle, WA

The Senior Staff Scientist will lead the development of hybrid AI applications on quantum computers, collaborate across teams to optimize algorithms, and represent IonQ at conferences. The role requires expertise in quantum AI applications and significant experience in algorithm development and business problem-solving.

As a Principal Data Scientist at Atlassian, you will analyze data to derive insights about products and customers, collaborate with cross-functional teams, and influence strategy through data-driven recommendations. You will develop hypotheses, apply statistical concepts, and measure the impact of product efforts using forecasting and key metrics analysis.

The Commerce Analytics Specialist II will analyze data to optimize customer funnels and enhance go-to-market strategies. Responsibilities include identifying trends, conducting performance analysis, developing strategies for campaign improvement, and providing forecasts to ensure successful execution of commerce initiatives.

As a Compensation Analyst, you'll participate in compensation surveys, analyze market data, maintain compensation databases, develop compensation models, and support compensation-related communications and policies. You'll collaborate with various teams to ensure equitable and competitive pay decisions.

As a Senior Applied Scientist at Chewy, you will develop machine learning models to enhance the advertising ecosystem. You will influence technical strategies for ads relevance, prediction, and optimization, lead projects from ideation to production, and mentor junior scientists. Collaboration with Product and Engineering leaders to improve advertising offerings and customer engagement is essential, along with sharing research insights and recommendations with leadership.

The Senior Data Scientist will collaborate with engineering and product teams to drive business growth through data-driven insights. Key responsibilities include analyzing customer behavior, optimizing products, developing segmentation strategies, and creating reports and dashboards to track revenue and engagement metrics.

You will develop machine learning models for fraud prevention, assess transaction risk, and work with engineering teams to implement robust scoring systems. Collaborate with data scientists to adapt to fraud trends, ensuring optimal model performance and feature integration.