• Data Management leadership on studies • Responsible for the development of project documentation, system set-up, data entry, and validation procedures • Assume responsibility for all DM activities from study start-up to database lock • Act as subject matter expert (SME) for DM activities in relationship meetings with Sponsors • Work directly with Sponsors to understand their requirements • Provide guidance, mentoring, and training to DM staff • Organize and effectively prioritize workload and deliverables • Ensure all projects are carried out in strict accordance with protocols, SOPs, and GCPs • Build timelines to meet contracted milestones • Provide DM project team leadership and accountability • Maintain awareness of other functional group deliverables to support risk and mitigation strategies • Develop and maintain client relationships and review client satisfaction surveys

• Oversee, lead, manage and provide technical expertise within the assigned projects to ensure that they are executed in an efficient, accurate and timely manner to the Sponsor’s satisfaction. • Provide fully independent and autonomous leadership of data management services (start up, conduct and close out) across multiple complex global projects/programs. • Prepare DM trial documentation (DMP, DB Specifications, Edit check specifications, CCGs, DTAs). • Ensure effective UAT is performed. • Write external vendor reconciliation specifications for programming reconciliation outputs (e.g. Serious Adverse Events, IRT, Central Laboratory). • Oversee data cleaning activities. • Monitor project scope, budgets and risks and alert DM Management of any concerns.

• Lead the design, development, and deployment of scalable machine learning solutions focused on pricing optimization, demand forecasting, and promotion planning. • Build and improve statistical and machine learning models for: - Demand forecasting - Price elasticity modeling - Promotion optimization - Inventory and revenue forecasting • Design, build and deploy robust ML pipelines in Databricks, build model monitoring systems, and production-ready APIs. • Ability to design and evaluate transformer-based time series forecasting models for large-scale retail sales forecasting and demand planning. • Drive experimentation, model evaluation, and continuous improvement of forecasting and pricing models. • Analyze large-scale structured and unstructured retail datasets to uncover trends, customer behavior patterns, and pricing insights. • Develop data-driven strategies that help maximize revenue, profitability, and pricing efficiency across products and categories. • Apply advanced statistical techniques and machine learning algorithms to solve complex retail business problems. • Collaborate closely with Product, Engineering, Data Engineering, and Business teams to translate business requirements into scalable ML solutions. • Mentor junior data scientists and provide technical guidance across cross-functional teams. • Communicate analytical findings and business recommendations clearly to both technical and non-technical stakeholders.

• Analyze product usage data to identify behavioral patterns, segmentation opportunities, and growth drivers • Develop predictive models to analyze customer churn, retention, expansion, and product adoption • Build customer health scores and predictive signals to support proactive customer success strategies • Apply appropriate AI/ML techniques (e.g., advanced segmentation, pattern detection, NLP where relevant) to enhance insights • Translate business questions into analytical frameworks and data science solutions • Partner with Customer Success and Product teams to identify opportunities to improve engagement and reduce churn • Generate actionable insights and recommendations to influence business decisions • Communicate complex analyses clearly to both technical and non-technical stakeholders • Collaborate with BI teams to embed predictive insights into dashboards and reporting