About the role
Since you’ve landed on this page, you already know Hedvig is a mover and shaker in insurtech, and think you might have what it takes to join us. We’ve been working hard since the very beginning to change an industry that is hard to love – to something at least likable. At Hedvig, we believe the only way to do so is to work with great people. What
about
the role? We are now looking for a Junior Analytics & ML Engineer to join our ambitious team at Hedvig, to develop our core data model, creating the fuel for solving complex business problems across the company, and building the infrastructure for our ML / AI platform. With agentic AI we are able to build these cornerstones faster than ever enabling fair pricing, reporting, product recommendations and our customer service bot. We believe, and know from experience, that we can accelerate faster by enabling ownership of the entire pipeline from raw data to deployed model, reaping the
benefits from developing with AI. This role is what makes that possible. Working closely with experienced backend developers, data scientists, data analysts, and various business teams, you will have the opportunity to shape the data architecture, ML platform, and Agentic AI development that fuel our growth. In
the role you will: Architect and scale our core data model, leveraging agentic AI to automate data pipelines, transformation, and ensure high-quality data Design and build robust ML and AI infrastructure, utilizing Agentic AI workflows to create a seamless, scalable platform for model deployment, MLOps, and continuous monitoring Drive data and infrastructure projects from project definition to deployed solutions, with full end-to-end ownership Have the opportunities to design and deploy your own machine learning and AI models, contributing directly to automated decision-making Be a driving force in our tech evolution by championing your work to stakeholders and always being on the lookout for new engineering trends within AI, the modern data stacks, and Insurtech. What about you? The Foundation: You hold a Master’s in Computer Science, Data Engineering, Engineering Physics, Mathematics, or a related quantitative field Potential experience: You have 0-2 years of experience working with a modern data stack like BigQuery, dbt and Looker, or deploying ML infrastructure that drives real business value The Data Foundation: You possess an understanding of data modeling, data warehousing, workflow orchestration, and is fluent in SQL to architect your own data preparation and feature engineering pipelines Scalable ML Mindset: You are proficient in Python and standard DS libraries, and you understand how to build and maintain a scalable ML architecture to transition from data to deployed models Production-Ready: You have some experience working with cloud platforms (GCP, AWS, or Azure) and embrace MLOps practices to ensure your models are scalable and robust The Generative Frontier: You have an interest in the modern AI stack, including LLMs, RAG, prompt engineering etc, to accelerate the development of data and ML platforms The Engineering Advocate: You thrive in collaborations, translating problems into rigorous scalable solutions, and love evangelizing modern data engineering, MLOps, and agentic AI Those are not hard