Top Data & Analytics Jobs

Analyze product performance and sales data, manage inventory, and collaborate with suppliers to optimize product sourcing and delivery.

The Data Engineer will clean, manage, and analyze datasets, automate ETL processes, and collaborate with traders and software engineers to derive insights.

As a Memory Developer, you'll record household tasks, shape the robot's memory, and help train its AI for better performance.

Design and maintain petabyte-scale data architectures and ETL pipelines; enable automated MLOps workflows; lead teams building ML systems and backend services; implement CI/CD and DevSecOps practices; integrate components, write maintainable Python code, and produce technical documentation while collaborating with cross-functional teams.

The Associate Director develops and executes pricing strategies, analyzing market dynamics, leading pricing initiatives, and ensuring compliance in the pharmaceutical industry.

The Business Analyst will analyze business processes, document requirements, test applications, and coordinate with project teams to develop information solutions.

The Senior Solutions Architect will lead the design and optimization of scalable data platforms, focusing on cloud-based environments, big data technologies, and data visualization for federal data analytics initiatives.

Own and automate pricing models, cost calculators, and margin reporting. Perform SKU- and order-level landed-cost and margin analysis, quantify pricing impacts, maintain pricing logic/templates, build SQL/BigQuery pipelines, automate reporting with R/Python and Google Sheets/Excel, and collaborate with cross-functional teams to improve pricing accuracy and recommendations.

The Senior Actuarial Analyst will perform complex actuarial analyses, assist in pricing and financial modeling, and support actuarial accreditation efforts. This role requires data acquisition, statistical modeling, and effective communication across departments.

Design, build, and maintain scalable data models and BI semantic layers. Partner with analysts and business teams to translate requirements, enforce modeling/testing standards, resolve data quality issues, mentor colleagues, and evaluate new data tooling to support dashboards, self-serve analytics, and AI solutions.