Data Analyst Jobs in Cambridge, MA

The Supply Chain Data Analyst will enhance operational efficiency by managing procurement, auditing supply chain workflows, and exploring automation and AI opportunities.

Build and productize externally consumable data assets from concept to delivery. Define schema, governance, SLAs, documentation, and delivery (Snowflake share, SFTP, API). Partner cross-functionally to productionalize datasets, monitor quality, iterate on customer feedback, and create repeatable productization frameworks.

Partner with Sales, Product, Engineering, and Operations to build the GTM analytics foundation for a new dealer software pillar. Own pipeline, install base, churn, demo-to-close, and launch analytics; identify and fix Salesforce/process gaps; create prospecting and churn-risk signals; write production-grade SQL on Snowflake and productionize assets in dbt; and maintain dashboards in Looker, Omni, and Salesforce CRM Analytics to drive measurable go-to-market execution.

Lead product analytics for the Dealer team: run exploratory analyses, build and audit metrics, create dashboards, automate data transformations (dbt/LookML), partner with Data Engineering, and present insights to influence product strategy. Use SQL, Python/R, and GenAI tools to solve complex data problems and improve product performance.

Account Executives at LinkSquares manage the entire sales process, focusing on new customer acquisition through research, outreach, and closing. They prioritize teamwork, quotas, and deadlines while driving business opportunities in a collaborative culture.

Develops and manages KPIs, dashboards, datasets, and analytics to measure Medical Affairs impact, resource allocation, and performance. Leads cross-functional analytics projects, mentors data analysts, manages vendors, improves reporting processes, and translates stakeholder feedback into strategic insights. Requires strong pharmaceutical medicine knowledge, including clinical trials, GCP, drug development, regulatory guidance, compliance, and promotional rules.

Clean, structure, and analyze primary market research data using statistical tools; produce client-facing cross-tabulations; perform survey and data quality assurance; maintain databases and data systems; and improve processes while collaborating with research, marketing sciences, and publishing teams.

As a Lead Data Science Analyst, you will develop predictive models, conduct statistical analyses, and create AI-driven solutions to enhance GTM strategy and customer outcomes.

Analyzes digital customer and product data to deliver dashboards, reporting solutions, experimentation measurement, journey analytics, attribution insights, and ad-hoc analysis. Partners with Digital Product and Technology teams throughout the product lifecycle, validates data and tracking solutions, supports platform modernization, performs data quality checks, and translates business questions into scalable business intelligence products while maintaining risk and regulatory controls.

Leads advanced people analytics using Workday as the foundation for workforce insights, planning, retention, compensation, and talent decisions. Develops analytical datasets, metrics, dashboards, predictive models, and executive reporting; establishes data definitions, governance, quality controls, and architecture standards. Partners with HR Technology, Finance, HR leaders, and business executives to translate complex workforce data into strategic recommendations. Serves as a Workday and people-data subject-matter expert, mentors analysts, and influences enterprise decisions through clear storytelling.

Own and improve federal data quality, fulfill customer data requests, build tailored data feeds and trackers, and identify patterns for customer insights and marketing thought leadership. The analyst will conduct QA across personnel, organizational, and program data, collaborate with data, product, and engineering teams, and translate data gaps into platform improvements. The role requires basic SQL, analytical writing, attention to detail, and comfort working across competing priorities.

Build and maintain the company-wide data foundation for FinOps; develop pipelines, dashboards, and operating scorecards; support stakeholders across commercial, finance, clinical ops, and operations; validate metrics with analytics engineers; transform messy datasets into actionable insights and adopt AI-powered workflows to improve efficiency.

Turn complex data into actionable insights for National Accounts by performing statistical analysis, forecasting, modeling, and automated reporting. Build dashboards and executive presentations, diagnose data quality, partner with account teams, data engineers, and finance to influence decisions and track performance metrics.

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.

Perform independent healthcare analytics projects for clients: use statistical tools, predictive modeling, BI/reporting and data visualization. Build reports and insights using R, SAS, SPSS, Cognos, and Business Objects, and collaborate with the Senior Consultant to maintain client relationships.

Lead client-facing analytics engagements: translate ambiguous business questions into data models, dashboards, and KPIs; write production-quality SQL and dbt models; build BI reporting; apply AI tools to accelerate analysis; drive proactive insights; set stakeholder cadence; and mentor junior analysts to ensure adoption and delivery.

Design, build, and maintain Power BI and Tableau dashboards; analyze large, complex datasets using SQL, Python, and statistical methods; partner with data engineering and business stakeholders to ensure data quality; translate findings into clear, actionable stories and executive-level presentations; support firmwide analytics growth and automation.

Support recruitment, enrollment, and classroom activities for employment and skills training; tutor and teach work-readiness; help clients create resumes and applications; perform data entry, intake, and administrative tasks; accompany clients to job events when appropriate.

Work on client onboarding/migration as a Data Analyst/Database Developer: gather business requirements, perform data analysis, mapping, profiling and query optimization. Coordinate with US/UK/India teams and vendors, ensure data quality via control scripts, identify source systems and eliminate redundancies. Assist BUAT, train new resources, and support extracts, views, and client reports. Financial services domain expertise required.

Conducts data collection, analysis, predictive modeling, and reporting for university research administration. Develops and maintains Tableau, Power BI, and Cognos dashboards; validates data across legacy and current electronic research administration systems; monitors data quality; supports system migrations; documents data pipelines and reporting logic; and improves reporting processes through automation and standardization. Collaborates with stakeholders to deliver accurate, actionable insights and supports ad hoc research analytics and AI-assisted data extraction.

Lead development of customer-facing analytics and internal dashboards by building advanced SQL models, optimizing visualizations, and maintaining data models. Drive analytics priorities, improve team processes, present findings to leadership, and participate in agile planning and peer review to support product, operations, and commercial teams.

Audit and improve human and model-generated data outputs for accuracy, consistency, and policy compliance. Provide actionable feedback, clarify evolving guidelines, support annotators, coach new team members, monitor workflows and blockers, identify recurring issues and edge cases, participate in calibration discussions, and document findings to improve team performance and quality at scale.

Analyze and maintain investment data, build quantitative models and performance attribution systems, automate reporting workflows, and create dashboards for investment teams. Partner with research, portfolio construction, risk, and technology stakeholders to translate business needs into reliable data solutions. Support data quality, model governance, backtesting, scenario analysis, factor research, process improvement, and evaluation of AI and machine learning capabilities.

Develop and maintain investment data solutions, quantitative models, performance attribution systems, dashboards, and reporting workflows. Partner with investment professionals to translate analytical needs into reliable data products supporting portfolio construction, manager selection, risk analysis, and research. Automate processes, improve data quality, apply model governance, and collaborate with technology and risk teams across global offices.

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.