Data Scientist Mid

The ideal Mid-Level Data Scientist candidate will have experience applying Natural Language Processing (NLP) and deep learning techniques within the insurance industry, focusing on data extraction challenges. They should be adept in traditional statistical learning and data analysis, capable of independently developing solutions using cloud platforms like Azure and programming in Python with version control through git. This role involves working directly with API-based generative AI models to create impactful solutions. The candidate will collaborate with cross-functional teams, mentor junior team members, and contribute to both technical and non-technical documentation. They should demonstrate strong communication skills, manage their time effectively, and navigate complex and varied technical challenges with ease.

Responsibilities

• Deliver production quality solutions for data extraction, classification, triaging, routing, and search
• Programmatically explore data, derive statistically sound insights, and effectively communicate findings to colleagues
• Lead data labeling and validation efforts using manual and AI-driven approaches
• Evaluate model performance, applying both practical and statistical benchmarks
• Collaborate with scientists, engineers, and product owners to develop solutions
• Mentor junior team members, guiding them through technical challenges
• Contribute to, improve, and convey technical and non-technical documentation

Qualifications

• Bachelor's degree in a quantitative field; master's preferred but not required
• Proven capability in NLP, deep learning, and strong skills in traditional statistical learning and data analysis
• Proficiency in Python programming, managing dependencies, and using version control in git
• Experience in a corporate environment applying quantitative methods
• Strong communication skills and ability to manage time effectively

Preferred Qualifications

• Insurance experience
• Experience with cloud computing platforms, especially Azure
• Understanding of generative algorithms and foundation models
• Experience evaluating ethical implications of AI

Bachelor's degree in a quantitative field; master's preferred but not required