Data Scientist || New Jersey (3 days onsite)

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
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.