• Execute and manage all in house clinical data management activities for assigned oncology trials in accordance with ICH/GCP, regulatory requirements, and internal SOPs • Support and contribute to the oversight of CROs and data management vendors, including review of deliverables, metrics, timelines, and issue escalation • Perform and/or oversee database build, edit check specification review, UAT planning and execution, database modifications, and database lock activities • Conduct ongoing data review, cleaning, query management, and quality control to ensure data accuracy, consistency, and completeness • Develop, review, and maintain Data Management deliverables, including: Data Management Plans (DMPs), CRF Completion Guidelines, Data Validation Specifications, and Data Transfer Specifications • Monitor study status metrics and provide regular data management status updates to study teams and functional leadership • Collaborate cross functionally with Clinical Operations, Medical Writing, Medical Monitoring, Biostatistics, and Safety to support data reconciliation, analysis readiness, and submission activities • Identify operational risks, trends, and data quality issues using metrics, audits, and study insights, and partner with teams to implement corrective actions • Support inspection readiness activities, audits, and regulatory inquiries related to clinical data management

• Build, iterate, and launch models to a production environment focused on predicting outcomes in sports. • Train machine learning models and build statistical modeling frameworks using custom methods, or using xgboost, sklearn, pytorch, etc. • Manage SQL queries to ensure data pipelines are efficient, accurate, and up to date. • Rigorously backtest models to ensure highly accurate predictions.

• Design, develop, and maintain scalable data pipelines using PySpark to efficiently process large volumes of data. • Develop, test, and optimize Machine Learning models for predictive analytics, including regression, classification, and clustering techniques. • Manage and deploy Machine Learning environments and workflows using AWS SageMaker, while leveraging Athena for data querying and analysis. • Integrate Machine Learning models into production pipelines, ensuring proper monitoring, maintenance, and continuous improvement through MLOps best practices. • Collaborate closely with cross-functional teams to understand business challenges and translate them into data-driven solutions. • Conduct advanced data analysis using Python and SQL to deliver actionable insights. • Work collaboratively with multidisciplinary teams using Git/GitHub and agile development methodologies.

• Develop and optimize machine learning models for recommendations and personalization • Build merchandising and product ranking models for eCommerce platforms • Design customer segmentation and scoring models within CRM systems • Translate business problems into analytical frameworks and solutions • Perform deep-dive analysis and create impactful data visualizations • Define and validate AI use cases with business stakeholders • Evaluate model performance using statistical and business metrics • Design and run A/B tests and experimentation frameworks • Monitor model performance and recommend optimizations • Collaborate with engineering teams to deploy and scale models • Work with marketing, eCommerce, and CRM teams to embed models into workflows • Communicate insights clearly to technical and non-technical stakeholders