Top Entry Level Data & Analytics Jobs in Boston, MA

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

Work with operations, IT, and client teams to manage business applications, schedules, SFTP accounts, and automated reports. Perform data imports/exports, cleansing, QA reporting, trend analysis, and process improvements while maintaining client communication and meeting productivity goals.

This role involves analyzing home renovation cost data and homeowner priorities, focusing on spending trends, emotional satisfaction, and resale value considerations based on surveys.

As a Remote Information Entry Clerk, you will enter and maintain database information, ensuring data accuracy, conducting audits, and supporting administrative operations.

Join Kled to help build the future of human data collection and design systems that influence AI data training through a consumer application.

Inovalon seeks candidates to join their Talent Community, emphasizing data-driven solutions to enhance healthcare outcomes and economics.

Design, build, and maintain scalable ETL/ELT pipelines and data models to populate the data warehouse. Write reusable SQL and Python to transform data, monitor pipeline reliability and quality, enable AI workflows, collaborate with stakeholders, and drive adoption of modern data stack technologies (DBT, Iceberg, Databricks). Support data governance, observability, and security practices.

Analyze conversational call flows and large datasets to optimize IVR and generative AI systems. Perform call testing, annotation, dashboard monitoring, and deliver data-driven insights and presentations to improve self-service, reduce agent handling, and enhance customer experience.

The Data & Analytics team at Surescripts transforms healthcare data into actionable insights, collaborating with various stakeholders to support critical decisions across the healthcare ecosystem.

Collect, validate, and maintain market data across asset classes; perform statistical analysis and modeling; ensure data accuracy through QA; troubleshoot discrepancies; produce reports, dashboards, and recommendations; partner with stakeholders to improve data models and analytics practices.