• Work on well-defined analytical tasks with guidance • Build strong foundations in data science and analytics • Deliver high-quality, reliable analytical outputs that support business decision-making • Contribute to the execution of data-driven initiatives by working on data preparation, analysis, and validation • Apply statistical and analytical techniques to solve business problems under guidance • Support the development of scalable and repeatable analytical processes • Collaborate with cross-functional teams to understand business context and align solutions with organizational goals • Communicate insights and findings through clear documentation and presentations

• Lead the design, development, deployment, and optimization of machine learning, predictive analytics, and AI-powered solutions. • Translate business challenges and opportunities into analytical approaches, model specifications, and measurable success criteria. • Apply advanced statistical analysis, machine learning techniques, and data science methodologies to solve complex business problems. • Analyze large, complex datasets to identify trends, patterns, opportunities, and actionable insights. • Develop and maintain model documentation, technical specifications, and implementation plans. • Stay current with emerging technologies, tools, and best practices in data science, machine learning, and artificial intelligence. • Design and execute comprehensive validation and evaluation strategies for machine learning and generative AI solutions. • Develop benchmarking frameworks and success metrics to assess model performance, reliability, and business impact. • Evaluate model quality using quantitative and qualitative measures, including accuracy, precision, recall, robustness, latency, and business outcome metrics. • Assess generative AI applications for response quality, grounding, relevance, consistency, and hallucination risk. • Identify and mitigate risks related to bias, fairness, explainability, privacy, and model reliability. • Perform model validation, testing, and performance assessments prior to production deployment. • Establish monitoring processes and evaluation methodologies to ensure continued model effectiveness and alignment with business objectives. • Design, execute, and analyze experiments, including A/B tests and statistical studies, to measure product and business outcomes. • Define key performance indicators and success metrics for machine learning and AI initiatives. • Measure and communicate the impact of analytical solutions through statistical analysis and quantitative methods. • Partner with stakeholders to define hypotheses, success criteria, and decision-making frameworks. • Use experimentation and data-driven insights to guide product, operational, and strategic decisions. • Collaborate with Engineering and Data Engineering teams to implement, operationalize, and scale models in production environments. • Monitor deployed models for performance degradation, model drift, data quality issues, and changing business conditions. • Recommend retraining, optimization, or replacement strategies based on model performance and evolving business needs. • Support the creation of scalable, maintainable, and reliable AI and machine learning solutions. • Ensure model deployment processes align with engineering best practices and operational requirements. • Partner with Product, Engineering, Analytics, and business stakeholders to prioritize opportunities and deliver high-impact solutions. • Communicate complex analytical findings and technical concepts to both technical and non-technical audiences. • Present recommendations, insights, and model performance results to leadership and project teams. • Support technical reviews, project planning, and delivery activities across cross-functional initiatives. • Contribute to knowledge sharing, documentation, and best practices within the data science organization. • Provide technical guidance and mentorship to junior team members and peers as needed.

• Deliver comprehensive data management services across all study phases • Independently own assigned data management studies and deliverables from startup through database lock, proactively identifying risks, resolving issues, communicating status, and escalating when appropriate • Ensure clinical databases are complete, accurate, and compliant with Sponsor and regulatory standards • Serve as primary Sponsor contact for data management activities • Lead data management communications and coordinate with internal and external teams to ensure timely delivery of study milestones and progress updates • Train site staff (CRCs, CRAs, PIs) and client teams on EDC systems • Oversee CRF lifecycle from design to final delivery • Conduct User Acceptance Testing (UAT) and ensure database setup aligns with specifications • Develop and manage essential study documents (e.g., Data Management Plan, CRF Completion Guidelines, Validation Plans, SAE Reconciliation Plans) • Review and clean clinical data, manage queries, and reconcile third-party data • Coordinate database lock and final data delivery • Identify risks and proactively resolve project issues • Provide exceptional service to internal and external stakeholders

• Partner with Product, Engineering, Design, and Operations to inform and shape product strategy, prioritization, and decision-making • Develop metrics, data models, and analytical frameworks that improve how teams understand customer behaviour and business performance • Identify opportunities to improve customer experience, growth, retention, and operational efficiency through data • Translate complex business questions into clear recommendations and actionable insights • Proactively surface trends, risks, and opportunities that help the team focus on the highest-impact work • Collaborate with Analytics Engineering to improve the quality, scalability, and usability of data foundations for the team