GenAI Senior Data Scientist

Hi I hope you are doing well. We have an urgent position listed below. Please send your most recent resume along with the expected rate if you are interested. Job Role: GenAI Senior Data Scientist Location: Onsite - NJ - Princeton/New Brunswick area Visa: USC/GC Job Description: Must have: Python Programming Pytorch NLP ML modeling/development GenAI SQL LLMs, RAG architecture, and agent AI 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 visualisation. • Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions. • Present information using data visualisation 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 the standardisation 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 modelling. 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"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 troubleshooting 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