The ideal Junior Data Scientist works alongside experienced team members developing solutions for insurance business problems using foundation models and data science fundamentals.

They take initiative on assigned tasks, own their work with close collaboration, and proactively communicate progress, blockers, and questions with team and leadership.

They are developing systems thinking skills-learning to identify component dependencies, measure outcomes, and iterate on solutions.

They work in Azure/Databricks using Python with git version control.

This is an early-career opportunity for candidates with strong foundations seeking growth in applied AI. Responsibilities:Collaborate with team members to support translation of business problems into technical solutions · Complete assigned tasks in solution development for data extraction, classification, routing, or search using foundation modelsManipulate and analyze data programmatically following established patterns, and communicate findings clearly Engineer foundation model prompts under guidance and evaluate using statistical metrics with support Implement evaluation components using precision, recall, F-1 scores, and accuracy with guidance · Document work clearly with team review Learn to identify component dependencies and contribute to problem decompositionStrong communication skills that proactively surface questions and blockers, and keep team informed of progressWorking knowledge of Python from a functional programming paradigm, willingness to learn dependency management, virtual environments, and git version controlBasic familiarity with cloud platforms and APIs, eagerness to learn Azure, Databricks, and foundation model APIsFoundational understanding of ML fundamentals (supervised/unsupervised learning, evaluation metrics) and statistical methods from coursework or early experienceExposure to foundation models through coursework or projects, willingness to learn prompt engineering and output validationDemonstrated curiosity and willingness to iterate on solutions, learn from failures, and seek guidance when needed0-3 years of relevant professional experience in data science, machine learning, or related fields; advanced graduate research or academic work may substitute for professional experienceBachelor's degree in quantitative field with exposure to systems concepts (Computer Science, Statistics, Economics, Physics, Mathematics, Operations Research, Computational Linguistics)Exposure to generative AI through coursework or projectsCoursework with classical ML algorithms and iterative problem-solvingPortfolio or GitHub demonstrating coding ability and problem decompositionAGSI was incorporated in April

2016.

We are committed to supporting the goals of Arch divisions through exceptional service delivery.

We pride ourselves on maintaining flexibility and responsiveness to adapt to business unit and industry demands while focusing on sound project management.

We are dedicated to growing and developing our employees as we build strong teams with strategic leadership.