• Spearhead the development of state-of-the-art natural language processing (NLP) and large language model (LLM) solutions • Mentor Associate Data Scientists in analyzing complex data • Perform independent and team-based research • Generate insights from large data sets • Interface with business leaders and data engineering teams • Contribute to model governance, documentation, and explainability frameworks

• Design and implement end-to-end ML pipelines • Develop and experiment with Deep Neural Network architectures • Translate ambiguous business requirements into technical solutions • Work with large datasets for acquisition, preprocessing, and analysis • Ensure code quality and maintainability • Collaborate with cross-functional teams to integrate ML solutions into production systems • Utilize CI/CD, version control, and automated testing in ML projects

• Under guidance of more senior data scientists, own the development and deployment of machine learning models to support operational and clinical use cases. • Perform ad-hoc data analyses related to outputs or alternative use-cases for existing models (e.g. summarize outputs for reporting purposes) • Work with program owners to understand the goals, key performance measures, and operational details of programs and processes to ensure models are aligned with program intent • Create and present proposals for machine learning solutions to program owners with the aim of ensuring buy-in to proposed solution. **Please note that employment will be contingent upon providing documentation verifying your legal work authorization in the country of residence, in accordance with applicable law.**

• Collaborate closely with Data Engineers and business stakeholders to define and validate curated datasets optimized for analytics and AI use cases. • Specify and document data requirements, feature definitions, and aggregation logic for downstream analytical and machine learning applications. • Validate data completeness, consistency, and statistical soundness of serving-layer datasets. • Provide continuous feedback to data engineering teams on data model usability, performance, and analytical fitness. • Perform exploratory data analysis (EDA) to identify patterns, anomalies, and data quality issues. • Build and test baseline machine learning models and analytical prototypes using curated datasets. • Contribute to the design and implementation of lightweight ML pipelines (training, evaluation, inference) aligned with platform standards. • Ensure features and models are reproducible, versioned, and prepared for future operationalization in production environments.