Senior Data Scientist (Experimental Design), Data Science Lab

Senior Data Scientist (Experimental Design), Data Science Lab

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 created a Data Science Lab (DSL) to reimagine insurance in light of emerging technology, societal shifts, and evolving consumer needs. The DSL will expedite Guardian's transition to data-driven decision making and insight generation, fostering long-term innovation. The DSL will establish rapid testing capabilities for new technology and the translation of pioneering research into practical, enterprise-wide solutions.

Guardian is seeking an experienced individual contributor with a strong background in experimentation and casual inferencing. 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. Successful candidates bring expertise in insurance and financial services, a passion for applying cutting-edge ML and AI insights, and the ability to design and implement data science capabilities that foster growth, competitive advantage, and customer satisfaction.

You Will:

• Develop Enterprise Test and Learn Capabilities
  • Investigating the current state of the art of experimentation practices and causal inferencing/ML techniques identifying opportunities for upscaling the methodology best practices
  • Develop and execute advanced data-driven experiments to optimize various aspects of Guardian’s business
  • Creation of test hypothesis, experiment design including KPI selection, and collection and analysis of data
  • Develop statistical and AI/ML models to analyze experimental data and derive actionable insights
  • Applying statistical methods to assess the reliability and significance of experimental results
  • Conduct A/B testing, multivariate tests, and other experimental methodologies to optimize customer experience, product features, marketing campaigns, and other business objectives
  • Organizing and managing data to extract insights that can be further incorporated into solution/model

• Support and help build the Data Science Lab (DSL)
  • 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

• Develop Deep Learning/Large Language Model/Generative AI capabilities
  • Mapping and mining unstructured data such as insurance contracts, medical records, sale notes, and customer servicing logs
  • AI/ML solutions include but not limited to enhancing underwriting risk assessment, claims auto adjudication, and customer servicing

• Contribute to the overall Data Science organization
  • Collaborate with cross-functional teams of other Data Science, Data Engineering, Business groups
  • Contribute to standardization of Data Science tools, processes, and best practices

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