Senior Data Scientist - Generative AI (Data Science Lab)

Senior Data Scientist (Generative AI), Data Science Lab

• Direct Hire
• Hybrid
  
• Office locations: NYC, NY | Holmdel, NJ | Stamford, CT | Bethlehem, PA, Boston, MA

We are seeking a Senior Data Scientist - Generative AI to be a contributor with a strong background in LLM and Generative AI. You will be responsible developing advanced data science solutions, leveraging machine learning and artificial intelligence, to drive enterprise-wide innovation across various business lines and Company’s products. You will collaborate with Data Science Tech Leads 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 the Company’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 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
  • Run large-scale experiments, from unsupervised pre-training, to fine-tuning, retrieval augmentation and prompt engineering
  • Scaling LLM models both in development and in production
  • Design and develop high-quality prompts and templates that guide the behavior and responses of LLM. Craft prompts to elicit specific information or control the model's output, ensuring desired accuracy, relevance, and language fluency. Optimize prompts to improve user interactions and system performance
  • Evaluate LLM models on statistical tests, business metrics, and bias an other regulatory metrics

• 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
  • Run large-scale experiments, from unsupervised pre-training, to fine-tuning, retrieval augmentation and prompt engineering
  • Scaling LLM models both in development and in production
  • Design and develop high-quality prompts and templates that guide the behavior and responses of LLM. Craft prompts to elicit specific information or control the model's output, ensuring desired accuracy, relevance, and language fluency. Optimize prompts to improve user interactions and system performance
  • Evaluate LLM models on statistical tests, business metrics, and bias an other regulatory metrics

• 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

• 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

• 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

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 the Company. You enjoy collaborating with a multi-disciplinary team including data engineers, business analysts, software developers and functional business experts and business leaders.

You have:

• 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
• Strong theoretical foundations in probability & statistics, and causal inferencing techniques
• Extensive experience in deep learning models including Large Language Models (LLM) and Natural Language Processing (NLP)
• Hands-on experience with GPU, distributed computing and applying parallelism to ML solutions
• Strong programming skills in Python including PyTorch and/or Tensorflow
• 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