Lead Data Scientist - Insurance

About the position

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. We've recently onboarded a Chief Data & Analytics Officer (CDAO) to guide this transformation and lead the Enterprise Data and Analytic Office (EDAO). This role presents a distinctive opportunity to collaborate with the CDAO and the Head of Data Science, actively contributing to Guardian’s ongoing evolution. Guardian’s EDAO spearheads a culture of data insights across Guardian, facilitating the successful realization of our strategic initiatives. Our core activities encompass creating business value from our data and analytic products. Key responsibilities include data lifecycle management, insight development, and data product delivery. We are a team of data analysts, data product owners, data engineers, data scientists and most importantly, data business leaders. Our solutions and actions are critical to Guardian’s revenue growth, risk management and customer experience. Guardian is seeking Tech Lead with a strong background in production ready AI/ML solutions. In this role as Lead Data Scientist, your responsibilities will include developing 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 senior executives on high-impact high-visibility projects to deliver AI/ML solutions that will be market-tested and deployed to make a real difference to risk management and Guardian's overall financial performance.

Responsibilities

• Build and lead a high performing group of data scientists
• Stay current with developments in ML and AI innovations, industry trends, challenges, and potential market opportunities
• Lead and mentor other team members
• Manage business stakeholders from ideation to deployment and aftercare
• 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
• Utilizing advanced statistical and AI/ML techniques to create high-performing predictive models and creative analyses to address business objectives and partner needs
• 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

Requirements

• PhD with 6+ years of experience, Master's degree with 8+ years of experience, or Bachelor's degree with 10+ years of experience in Computer Science, Engineering, Applied mathematics or related field
• 7+ years of hands-on ML modeling/development experience
• 2+ Years experience working within Insurance Industry
• Knowledge of Individual Markets or Direct to Consumer Insurance experience is a major plus
• Extensive experience in deep learning models including Large Language Models (LLM) and Natural Language Processing (NLP)
• 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 design and executions
• Hands-on experience with data wrangling including fuzzy matching and regular expression, distributed computing and applying parallelism to ML solutions
• Strong programming skills in Python
• Solid background in algorithms and a range of ML models
• Excellent communication skills and ability to work and collaborating cross-functionally with Product, Engineering, and other disciplines at both the leadership and hands-on level
• Excellent analytical and problem-solving abilities with superb attention to detail
• Proven leadership in providing technical leadership and mentoring to data scientists and strong management skills with ability to monitor/track performance for enterprise success

Benefits

• Support and flexibility to achieve professional and personal goals
• Skill-building, leadership development and philanthropic opportunities
• Contemporary, supportive, flexible, and inclusive benefits and resources