Top Entry Level Data & Analytics Jobs

Supports data pipeline development, data modeling, wrangling, semantic layers, dashboards, and reporting using SQL, Python, AWS, and Snowflake. Collaborates with cross-functional teams to translate business requirements into data solutions and applies data governance, security, and quality practices. This co-op is designed for a currently enrolled university student available January 11 through June 25, 2027, and willing to relocate to Framingham, Massachusetts.

Designs, develops, and maintains scalable data pipelines, transformations, datasets, and curated data products for credit risk, reporting, analytics, decisioning, and machine learning. The role uses SQL, Python, Databricks, and Spark; implements data quality controls; supports production monitoring and troubleshooting; collaborates with analytics, data science, product, and engineering teams; and contributes to cloud migration and data platform modernization.

Own Tomo’s data function, strategy, roadmap, platform health, and cross-functional measurement. Build and lead teams spanning data science, analytics, business intelligence, and data engineering. Design experiments, marketing attribution models, SQL queries, and dbt models while maintaining reliable data infrastructure. Partner with Product, Engineering, and executives to improve instrumentation, translate business questions into insights, and establish scalable data processes.

Designs conceptual, logical, and physical data models for R&D research and clinical domains. Translates scientific and business requirements into reusable, scalable, AI-ready data structures that support analytics, machine learning, data integration, and standardized workflows. Partners with domain experts, data engineers, architects, data scientists, and governance teams to apply enterprise standards, schemas, ontologies, metadata, quality rules, and lineage. Promotes shared data assets and ensures models are implementable, interoperable, and aligned with evolving R&D needs.

Analyze and resolve pricing control exceptions involving fees, interest, refunds, and servicing activities. Review processes, build automated analytical workflows, develop reporting, and apply anomaly detection and responsible generative AI concepts. Partner with analysts, managers, product, operations, analytics, and technology teams to improve monitoring, documentation, and controls. Communicate findings clearly, manage multiple workstreams, and drive continuous improvement across card pricing operations.

Develops automation, data pipelines, databases, analytics, machine learning models, reporting tools, and web applications for package technology quality assurance. Analyzes manufacturing, product, and reliability data to support risk assessment and root-cause investigations. Collaborates with engineering teams, communicates findings, trains users, maintains software and reports, and shares analytics best practices across global teams.

Analyze marketplace and product performance using SQL, support experiments and advertising-effectiveness studies, build reusable datasets, QA metrics, and document analytics. Collaborate with analytics engineers, data scientists, product managers, engineers, and commercial teams while developing skills in statistics, causal inference, Python/R, and marketing science.

Designs and maintains data visualization, reporting, and business intelligence platforms. Builds Python frameworks, dashboards, reporting applications, and analytics solutions for traders, quants, and developers. Optimizes data access and query performance, supports Looker, BigQuery, Plotly Dash, and Jupyter Notebooks, translates stakeholder requirements into technical solutions, and leads migrations across tools, libraries, and data sources.

Conduct quantitative research to design, implement, and deploy mathematical trading models. Build custom tools for exploratory analysis, apply rigorous quantitative techniques to solve complex problems, and collaborate with traders and developers to productionize models while maintaining best practices.

Develop and advance the enterprise data platform by gathering business requirements, designing data models and warehouses, and building data visualization solutions. Administer reporting, analytics, and visualization platforms while promoting best practices. Collaborate cross-functionally, apply business and process knowledge, manage multiple projects, communicate strategically, and provide technical leadership in data engineering and business intelligence.