Senior Data Scientist, AI Studio

Overview

Guardian is on a transformation journey to evolve into a modern, forward-thinking insurance company committed to enhancing the wellbeing of its customers and their families. This role presents a distinctive opportunity to drive real-world impact by applying cutting‑edge AI to transform how Guardian does business.

Guardian’s AI team spearheads a culture of intelligence and automation across the enterprise, creating business value from advanced data and AI solutions. Our team includes data scientists, engineers, analysts, and product leaders working together to deliver AI‑driven products that power growth, improve risk management, and elevate customer experience.

Guardian is seeking an experienced individual contributor with a strong background in production‑ready AI/ML solutions. As Senior Data Scientist, you’ll be working in a team of Data Scientists led by Lead Data Scientist to develop advanced data science solutions, leveraging machine learning and artificial intelligence, to drive enterprise‑wide innovation across various business lines and Guardian products. You’ll collaborate with cross‑functional teams of other Data Science, Data Engineering, Business groups.

Successful candidates have strong technical foundation, excel in coding, detail oriented, and have passion for analytical thinking and problem solving.

• Lead use case/workstream with junior data scientists
• Support use case development that includes initial data exploration, project/sample design, reception and processing of data, performing analysis and modeling to creation of final report/presentation
• Data wrangling/data matching/ETL to explore a variety of data sources, gain data expertise, perform summary analyses and prepare modeling datasets
• Utilizing advanced statistical and AI/ML techniques to create high‑performing predictive models and creative analyses to address business objectives and partner needs
• Identification of source data and data quality checks both in model/solution development and in production
• Packaging of model/solution and deployment in cooperation with Data Engineers and MLOps
• Implement new statistical or other mathematical methodologies as needed for specific models or analysis.
• Propose innovative ways to look at problems through using data mining and data visualization
• Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
• Present information using data visualization techniques; communicate results and ideas to key decision makers.
• Ensure data accuracy and consistent reporting by performing regular data quality control, prepare and maintain reports, and troubleshoot data anomalies
• Consistent accuracy and thoroughness in performing work assignments
• Attend industry conferences to stay current on industry trends, challenges, and potential market opportunities
• Contribute to standardization of Data Science tools, processes, and best practices

You will

• You are passionate about cutting‑edge technology and keen on applying new AI/ML algorithms and approaches.
• You are analytically driven, intellectually curious, and experienced leading the development and implementation of data and analytic solutions to solve challenging business problems.
• You enjoy collaborating with other data scientists to crack hard to solve problems with AI/ML and seeing it deployed in‑market and generating value for Guardian.
• You enjoy collaborating with a multi‑disciplinary team including data engineers, business analysts, software developers and functional business experts and business leaders.

• PhD with 2+ years of experience, Master’s degree with 4+ years of experience in Statistics, Computer Science, Engineering, Applied mathematics or related field
• 3+ years of hands‑on ML modeling/development experience
• Solid understanding of data analysis and statistical modeling.
• Knowledge of a variety of machine learning techniques (clustering, decision tree, bagging/boosting artificial neural networks, etc.) and their real‑world advantages/drawbacks.
• Demonstrated track records in experimental…