Top Remote Data & Analytics Jobs in Boston, MA

The Data Scientist II will lead and execute health economic outcomes research projects, collaborate with stakeholders, and build analytical infrastructure while adhering to regulations.

Designs and maintains SQL/Python data pipelines, warehouse models, and Power BI dashboards supporting finance planning, forecasting, budgeting, and book close. Owns solution architecture from requirements through deployment, validates data and systems, resolves process and reliability gaps, and manages multiple enhancements. Collaborates with business and technical stakeholders, mentors analysts, leads continuous improvement, documents solutions, trains users, and develops automated and AI-enabled efficiencies.

Leads enterprise data and analytics strategy, architecture, modernization, and delivery across cloud and on-premises environments. Oversees data platforms, engineering, integration, governance, quality, lineage, security, and compliance. Partners with executives to define roadmaps and investments, manages cross-functional teams and vendors, enables AI/ML capabilities, and mentors data professionals while driving scalable, trusted data solutions and measurable business outcomes.

Develops and maintains Access databases, SQL reports, Cognos and Power BI dashboards, SharePoint solutions, and operational workflows. Analyzes transactional, financial, staffing, productivity, and performance metrics; validates data; creates projections and regulatory reports; and presents findings to senior leadership. Partners with stakeholders and IT to gather requirements, improve processes, automate workflows, ensure data integrity, and support compliance and business operations.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Manages programmatic analytics and insights for client accounts, leading measurement frameworks, brand lift studies, incrementality testing, and A/B experiments. Develops media and campaign strategies, communicates performance insights to clients, and supports pitches and QBRs. Oversees, mentors, and develops analysts while partnering with Product, Engineering, Sales, and Customer Success teams to improve measurement tools, campaign optimization, and analytical capabilities.

Leads global contact center capacity planning, forecasting, headcount, and hiring strategies. Builds predictive staffing models using Python, analyzes contact flows and staffing accuracy, and develops reports with MicroStrategy, Alteryx, and Power BI. Communicates data-driven recommendations to executives, partner managers, vendors, and operations teams while managing planning initiatives and optimizing service levels, customer experience, and costs.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Analyzes operational and vehicle inspection data, builds and maintains reports, dashboards, queries, metrics, and data processes, and delivers insights to Operations and leadership. The role cleans and transforms large datasets, validates data quality, supports data science and engineering teams, develops monitoring systems, and presents trends and recommendations for business decisions. It also partners with product, dispatch, quality assurance, and technology teams on operational data and reporting needs.

Owns fraud performance analytics for depository products, including monitoring losses, approval rates, false positives, customer friction, and risk metrics. Builds data models, transformation pipelines, dashboards, scorecards, and monitoring frameworks. Conducts fraud deep dives and develops optimized rules, policies, thresholds, models, and decision strategies. Partners with Product, Engineering, Machine Learning, Data Engineering, Fraud Operations, and Risk teams to improve fraud detection and support product launches.

Lead design and implementation of scalable Databricks Lakehouse solutions using Delta Lake, Unity Catalog, and MLflow. Advise stakeholders, drive cloud data platform modernization across Azure/AWS/GCP, ensure data governance and quality, optimize performance and cost, and mentor teams. Support presales, deliver billable consulting, and champion AI/ML-enabled data solutions to translate platform capabilities into business outcomes.