• Perform Advanced Modeling & Analysis • Execute assigned data science projects with defined scope, delivering accurate and timely results • Develop and implement statistical and machine learning models under guidance • Apply appropriate analytical methods based on provided problem definitions, datasets and evaluation criteria • Utilize interactive development environments (e.g., notebooks) to explore data, prototype models and validate approaches • Interpret model outputs and assist in translating findings into actionable insights for stakeholders • Leverage AI-assisted development tools to accelerate analysis, research and code development • Perform AI Engineering • Utilize AI tools and large language models (LLMs) to support analysis, code generation and research tasks • Apply basic prompt engineering techniques to improve outputs from AI-assisted workflows • Assist in building simple AI-enabled workflows or components under guidance • Develop familiarity with emerging AI techniques and their application within data science workflows • Contribute to the development and maintenance of data science products, including models, pipelines, reporting • Support implementation of model training, evaluation and output generation workflows • Assist in documenting data science processes, model assumptions and system behavior • Generate regular reporting and analysis to support performance monitoring of data science products • Clean, transform and prepare large structured and unstructured datasets for analysis and modeling • Write SQL and Python-based transformations to create usable datasets from raw data sources • Work with Snowflake and dbt workflows to support development of analytics-ready datasets • Assist in developing and maintaining data pipelines used in modeling workflows • Perform exploratory data analysis to understand data characteristics, distributions and potential issues • Follow established software development best practices, including version control, documentation and testing standards • Write clear, maintainable and testable code for data science workflows • Assist in validating datasets, model outputs and reporting results for accuracy and consistency • Participate in code reviews, focusing on syntax correctness and general structure • Ensure reproducibility of analyses through proper documentation and organization of work • Develop proficiency in Python as the primary programming language and use R where appropriate for statistical analysis • Utilize standard data science libraries and frameworks (e.g., scikit-learn, pandas, numpy) • Begin developing modular code practices and reusable functions • Gain familiarity with containerization (e.g., Docker) and deployment concepts • Build foundational business acumen by understanding how data science outputs support business decision-making • Communicate findings and progress clearly with team members and stakeholders • Seek feedback and continuously improve technical and analytical skills • Collaborate effectively with other Data Scientists, Analytics Engineers and business partners • Deliver accurate, reliable and well-documented analytical outputs • Support team members by providing consistent, high-quality work • Develop expertise in assigned datasets, tools and processes • Build strong working relationships within the Data Science team and with cross-functional partners