Data Scientist - GenAI & ML

Must have: Python Programming Pytorch NLP ML modeling/development GenAI SQL LLMs, RAG architecture, and agentAI frameworks Key Responsibilities: Lead use case/workstream with junior data scientists 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. Required Skills: 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 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" #J-18808-Ljbffr