Data Scientist

• Contribute to the end-to-end model lifecycle, including data exploration and understanding, feature engineering, model training and validation, ensuring quality, security, scalability, and fairness
• Support use case development that includes initial project scoping, 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
• Adhere to model governance, documentation, testing, and other best practices in partnership with key stakeholders.
• 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
• Build LLM/AI powered application prototypes with lightweight UI (e.g., Streamlit) to validate usability and support adoption.

You are:

• Passionate about cutting-edge technology and keen on applying new AI/ML algorithms and approaches.
• Analytically driven, intellectually curious, and experienced leading the development and implementation of data and analytic solutions to solve challenging business problems.
• 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.
• 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
• Experience in Insurance Underwriting
• 3+ years of hands-on ML modeling/development experience
• Background in insurance and underwriting preferred
• 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 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 collaborate 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 experience in providing technical leadership and mentoring to data scientists and strong project management skills with ability to monitor/track performance for enterprise success
• Experience communicating complex ideas simply, presenting impact, trade-offs, and recommendations to non-technical partners.
• Working knowledge of core software engineering concepts (version control with Git/GitHub, testing, logging, …).
• Working knowledge of NLP, LLMs, RAG architecture, and agent frameworks, including safe automation design and evaluation systems.
• Experience in insurance, financial services, or related industries is a plus.