2026-7837 Data Scientist-Junior

Job Description

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 models
• Manipulate 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 decomposition

Qualifications

• Strong communication skills that proactively surface questions and blockers, and keep team informed of progress
• Working knowledge of Python from a functional programming paradigm, willingness to learn dependency management, virtual environments, and git version control
• Basic familiarity with cloud platforms and APIs, eagerness to learn Azure, Databricks, and foundation model APIs
• Foundational understanding of ML fundamentals (supervised/unsupervised learning, evaluation metrics) and statistical methods from coursework or early experience
• Exposure to foundation models through coursework or projects, willingness to learn prompt engineering and output validation
• Demonstrated curiosity and willingness to iterate on solutions, learn from failures, and seek guidance when needed
• 0-3 years of relevant professional experience in data science, machine learning, or related fields; advanced graduate research or academic work may substitute for professional experience

Additional Information

• Bachelor'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 projects
• Coursework with classical ML algorithms and iterative problem-solving
• Portfolio or GitHub demonstrating coding ability and problem decomposition