Top Hybrid Data & Analytics Jobs in Boston, MA

Lead data science projects to optimize drug development using AI. Develop models for patient selection and clinical trial evaluations while collaborating with cross-functional teams to drive insights.

The role involves developing statistical models and analyses to address client business issues, guiding decision-making with data insights and mentoring associates.

As a Business Analyst, you will drive process optimization by collaborating with teams, analyzing current operations, and implementing innovative solutions using AI and automation tools.

Design the architecture for a healthcare data ecosystem, ensuring integration of structured and unstructured data for AI use, while maintaining data quality and governance.

The Senior Analytics Engineer will own data pipelines and models for Sales and Product analytics, ensuring data quality and governance. They will collaborate with cross-functional teams to facilitate data-driven decisions and enhance productivity through scalable data solutions.

The Principal Front-Office Engineer will lead the design and implementation of AI-driven solutions in investment strategies, collaborating with analysts to enhance toolsets and reporting systems.

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 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.

The Data Engineer II will design data models, build ETL pipelines, collaborate with teams, and ensure data flows are efficient and well-monitored. A strong understanding of SQL, Python, and cloud technology is essential.

The Senior Data Engineer will design and optimize large-scale batch and streaming data pipelines using Scala and Spark, oversee the data engineering lifecycle, and engage in technical discussions to improve system efficiency.