Data Analyst Jobs in Cambridge, MA

Analyze and validate large benchmarking datasets, improve methodologies, support product enhancements, and engage directly with clients and sales to drive adoption and inform product strategy.

The Workday System Analyst is responsible for maintaining Workday data structures, configuring business processes, conducting data audits, and supporting system integrity and availability efforts.

Validate and assure enterprise data quality across source systems, ETL/ELT pipelines, data warehouses, and reporting. Investigate production incidents, design test strategies and automated validation, perform regression and non-functional testing, analyze data quality metrics, support compliance/audit activities, and collaborate with engineering and business stakeholders to improve data governance and controls.

Lead Wellington's market data program, managing vendor relationships, contract negotiations, onboarding/renewals, inventory and invoice workflows, licensing/reporting compliance, budgeting, and system improvements (Optimize Spend, ServiceNow). Provide stakeholder support, training, and drive process improvements to maximize value and control costs.

Senior Addepar data engineer/analyst responsible for supporting and scaling Addepar integrations, building and maintaining data pipelines (Addepar↔Snowflake/Canoe/other platforms), diagnosing and resolving data quality issues, developing SQL-based reconciliations and automation, partnering with business and technology stakeholders, and supporting production operations, governance, testing, and documentation for wealth and asset management data.

Lead research and advisory work on data foundations for analytics and ML: produce 8–12 research assets annually, drive vendor evaluations (Forrester Wave), consult clients on data/AI strategy, present at events, support business development, and foster cross-team collaboration. Focus areas include data quality, governance, context-ready data, large-scale data management, and measuring business outcomes from analytics and ML.

Build statistical and machine learning models on healthcare claims, financial, and organizational data to generate account intelligence. Develop entity resolution, affiliation/network mapping, clinical ontologies, and account-targeting signals. Design data-sourcing and scoring methods, partner with product and engineering to productionize models, and communicate results to varied audiences.

The Product Operations Analyst analyzes product data and customer behavior to inform strategy, supports performance tracking, and drives continuous improvement through data insights and experimentation.

The Business System Specialist will manage LIMS master data, provide end-user support, ensure compliance, and assist in process development for lab data management.

Own end-to-end API integrations (ingest and export) for customer data into CoStar Debt Solutions: scoping, credentialing, mapping, reliability, monitoring, and customer support. Build production-grade REST/GraphQL/webhook pipelines, scripts for transform/validation, and evolve integrations into agent-callable typed contracts.

Maintain and evolve GMO's security master and portfolio data platforms (NeoXam DataHub, Aladdin, Eagle PACE). Analyze large datasets using Python, SQL, and Excel to identify data quality issues, define business requirements, support conversions/migrations, implement controls/automation, and collaborate with stakeholders to modernize data validation and onboarding processes.

Audit and review data labeling outputs, provide actionable feedback, train and support labeling teams, monitor workflows, identify recurring issues and edge cases, participate in calibrations and stakeholder syncs, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and ensure quality of data labeling outputs, coach and onboard Data Labeling Associates, provide actionable feedback, monitor workflows, identify patterns and edge cases, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and validate data labeling outputs, provide structured feedback and hands-on training to annotators, monitor workflows and escalate issues, help apply and improve guidelines, and identify patterns in human and model outputs to maintain high-quality AI training data.

Audit and ensure quality of labeled data by reviewing DLA outputs, providing feedback and coaching, supporting onboarding and training, monitoring workflows, identifying recurring issues and edge cases, and contributing to guideline and process improvements in human-in-the-loop AI workflows.

Audit and improve data labeling quality in human-in-the-loop ML workflows. Coach and onboard labelers, provide actionable feedback, monitor queues, identify recurring issues, and help refine guidelines and processes.

Audit and ensure quality of data labeling outputs, coach and onboard annotators, enforce and improve guidelines, identify recurring issues and edge cases, escalate risks, and document feedback to support human-in-the-loop AI workflows.

Audit and ensure quality of data labeling outputs, coach and onboard annotators, apply and refine guidelines, monitor workflows and escalate issues, document findings, and support human-in-the-loop AI evaluation processes.

Audit and monitor data labeling outputs, provide actionable feedback, support onboarding and training of annotators, enforce and improve guidelines, escalate workflow issues, and document findings to maintain high-quality human-in-the-loop AI data processes.

Audit and ensure quality of data labeling work for ML using human-in-the-loop processes. Provide feedback, train and support annotators, monitor workflows, identify recurring issues, and help improve guidelines and processes.