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Develop and maintain fullstack features: design UIs, build robust APIs, write and test clean code, participate in deployments and monitoring, and contribute to architectural improvements and modernization efforts.

Manage onboarding, adoption, and renewals for insurance clients by leading rollouts, coordinating internal resources, advising Anti-Fraud/Claims/Underwriting teams, tracking KPIs, delivering executive business reviews, driving retention and growth, and surfacing customer feedback to product and leadership.

The internship involves deploying data processing pipelines, collaborating with clients, conducting R&D in AI and data science, and supporting sales efforts.

Design, build and maintain scalable data models, ETL pipelines, and data access layers. Collaborate with product and data science teams to enable analytics and dashboards, participate in agile processes, code reviews, deployment, and monitoring to deliver production-quality data solutions.

As a Fullstack Engineer, you will design user interfaces, build and maintain APIs, write and test code, and contribute to system improvements, collaborating with cross-functional teams.

Support deployment and maintenance of fraud-detection solutions: assist with ETL configuration, detection implementation, feature rollout, pipeline optimization, and cross-team client engagement under senior data scientists.

Build and deploy production ML solutions for fraud detection, AML and claim automation. Implement data pipelines from client extracts to the data warehouse, develop/tune/test models, automate business tasks in C#, report KPIs, and engage with clients and stakeholders.

Own marketing campaign execution, infrastructure, and analytics: build data workflows and automations between HubSpot and Salesforce, optimize digital campaigns, manage marketing budget and media spend, implement reporting dashboards, and partner with sales and field marketing to improve pipeline and ROI.

As part of the data science team, you will analyze insurance data, create mathematical models, and optimize algorithms based on client feedback.

Design and implement scalable features, build data pipelines, collaborate with cross-functional teams, and enhance data infrastructure for improved decision-making.