Top Hybrid Senior Level Data & Analytics Jobs in Boston, MA

Lead and grow an analytics engineering team to deliver scalable data models and reporting for Product, Marketing, Finance, and other stakeholders. Drive BI platform vision, collaborate with Product and Data Governance, prioritize work, implement agile practices, and ensure high data quality and performant transformations.

The Ecommerce Analyst drives analytics and insights for eCommerce growth, focusing on merchandising and marketing channel performance. Key responsibilities include analyzing revenue drivers, building data-driven business cases, and collaborating cross-functionally to enhance decision-making processes.

The Director of Biostatistics leads a team in clinical research, developing statistical plans, collaborating across departments, and ensuring regulatory compliance.

The Sr. Analytics Engineer at Philo is responsible for designing data models, governing business metrics, collaborating with stakeholders, and ensuring data quality and documentation for analytics purposes.

Lead the development of scalable data platforms and analytics systems, overseeing teams in building data pipelines, advanced algorithms, and dashboards for data-driven decision making.

Lead end-to-end analytics for Veterinary Diet, Science Diet, and Health & Wellness businesses. Build self-service dashboards, define KPIs, run deep-dive analyses, partner with technical and category teams, and deliver insights that improve customer experience and financial P&L.

The Manager of Advanced Analytics & Automation leads the strategic analytics and AI automation initiatives for customer intelligence, building data models, insights, and automation workflows to enhance customer success and retention.

Manage the data architecture and strategic planning for the Sales team, optimizing processes through data and AI for targeting accounts and pricing strategies.

Oversee client proposals and projects, leveraging coding skills for data processes, mentoring teams, and identifying growth opportunities in life sciences.

Design and evaluate marketplace algorithms for matching, pricing, and forecasting. Build price-estimation and demand/supply models, clean and analyze operational data, run simulations and counterfactuals, and collaborate with engineering to deploy model-driven systems that scale.