Top Hybrid Data & Analytics Jobs in Boston, MA

As a Senior Data Engineer, you'll design scalable data models, build automated ETL pipelines, and mentor junior engineers while collaborating across teams.

The role involves operating and improving AI-driven data quality pipelines, managing offshore teams, and ensuring data accuracy and reliability. Responsibilities include monitoring pipeline performance, refining rules, and collaborating across teams to enhance data operations.

As a Data Engineer II at Magnite, you will build and optimize data pipelines with Apache Spark and Python, collaborate on data architecture, ensure operational performance, and work on infrastructure provisioning using Terraform and AWS.

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.

The role leads Clinical Data & Information Sciences in drug development, ensuring data integrity, compliance, and continuous improvement while collaborating with cross-functional teams to enhance business processes and experience.

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

The Director, Forecast & Research will lead forecasting, market research, and insights generation for Xenon's epilepsy portfolio, collaborating with cross-functional leaders to deliver actionable analytics.

As a Senior Statistical Programmer, you will lead and support clinical trials by generating statistical programming deliverables, performing data analysis, and collaborating with cross-functional teams in a pharmaceutical setting.

The Biostatistician I will assist in the statistical analysis of clinical trials and studies regarding HIV and other infections, under the guidance of senior statisticians. Responsibilities include data analysis, maintaining study standards, and collaborating on publications.

Lead development of large-scale data analysis and validation workflows for autonomous driving ML. Design metrics, build datasets, run statistical error and regression analyses, collaborate with ML and software teams, and maintain AWS/Kubernetes orchestration for large-scale evaluation.