• Serve as the primary CTI Clinical Data Management (CDM) contact with sponsors • Represent the DM group on CTI project teams • Responsible for study-specific CDM document development and maintenance • Project status communication to the CTI project team, CDM management, and sponsors • Coordinate the receipt and processing of information for projects • Monitor the quality of work performed by the CDM project team • Develop project reports for sponsors, project team, and CTI management upon request

• Define and lead the enterprise AI and data science strategy — aligned to Duncan's Enterprise Goals, corporate growth objectives, and the imperative to deliver client value through our products and services • Advise the CTO and executive leadership team on opportunities to leverage artificial intelligence, machine learning, and predictive analytics to drive competitive advantage, margin improvement, and operational scale • Translate strategic business priorities into high-impact data science programs with clearly defined financial and operational outcomes — not exploration for its own sake, but applied innovation with measurable returns • Evaluate emerging technologies and industry trends to inform long-term digital strategy and investment decisions, bringing forward evidence-based recommendations rather than reactive responses to market noise • Oversee the design, deployment, and scaling of advanced analytics solutions within AWS-native environments — supporting revenue optimization, compliance oversight, customer experience enhancement, and operational efficiency • Champion automation and intelligent workflow transformation, including natural language processing, anomaly detection, and AI-driven operational optimization across Duncan's core service lines • Ensure analytics solutions are production-ready, scalable, and maintained with the operational rigor expected of enterprise systems — not prototype-quality deliverables deployed into live environments • Establish and maintain governance frameworks for model development, validation, monitoring, explainability, and ethical AI use — ensuring regulatory alignment and enterprise risk management at every stage of the analytics lifecycle • Ensure responsible AI practices are embedded in team culture and delivery methodology — not treated as compliance overhead, but as a professional standard that protects both Duncan and the clients it serves • Identify and surface emerging data science risks — model drift, data quality degradation, bias exposure, or governance gaps — before they become material issues for the enterprise or its clients • Partner with Legal, Compliance, and the CTO on AI-related regulatory developments, ensuring Duncan's practices remain defensible, auditable, and ahead of evolving requirements • Build and maintain executive-level dashboards and reporting frameworks that convert data science outputs into decision-enabling intelligence — giving the CTO, CEO, and enterprise leadership a clear, timely view of operational performance and AI-driven opportunity • Ensure data science insights are communicated in plain, accessible language that enables non-technical leaders to act with confidence — translating model outputs into business narratives, not statistical summaries • Support board-level reporting on AI strategy, innovation initiatives, and enterprise performance impact as directed by the CTO • Recruit, develop, and retain high-performing data science and analytics professionals — building a team culture defined by accountability, intellectual rigor, and continuous improvement • Establish clear performance expectations, development pathways, and capability-building investments aligned to Duncan's Job Group Family Architecture and the career development framework being built enterprise-wide • Ensure the data science team operates as a trusted enterprise partner — responsive to business needs, disciplined in delivery, and consistent in the quality of its work • Model the leadership behaviors Duncan expects at the senior leader level: operating with independence, developing the people around you, and connecting your team's daily work to the organization's long-term direction

• Help build and improve AI chatbots by training them with real conversation data • Clean and organize text data to make better machine learning models • Test how well our virtual agents are performing and find ways to improve them • Work alongside experienced consultants, developers, and conversation designers • Work with customers to understand requirements, pain points and clearly communicate progress and roadbloacks • Stay curious about new AI and NLP techniques

• Define the details of and execute the data science technical strategy, aligned to business objectives and overall data/analytics senior leadership vision and strategy. • Collaborate on the data science roadmap with data & analytics product management and stakeholders. • Mentor peer team members for effective delivery as well as to help build a high-performing data science and consultative business insights team. • Provide cross team technical reviews, thought partnership, and coaching. • Deliver effective and sustainable ML and GenAI products that drive measurable business value. • Lead cross domain programs that create reusable platforms, shared modeling components, and standardized evaluation frameworks. • Establish technical guardrails for modeling, feature engineering, experimentation, and MLOps across teams. • Design scalable ML/GenAI systems and reference designs, balancing performance, latency, cost, and compliance. • Review and approve models, ensuring rigor in causal inference, statistical methods, monitoring, and evaluation. • Introduce new methods and technologies when needed to unlock value or resolve domain‑level challenges. • Guide teams in designing robust experiments to measure outcomes and value. • Plan work and help with managing team capacity with data product owners/management. • Define MLOps strategy, including feature stores, observability, drift/performance monitoring, retraining policies, and rollback protocols. • Partner with engineering and platform teams to select tools, build roadmaps, and set SLAs for data quality, reliability, and model governance. • Ensure compliance with privacy, security, and Responsible AI requirements. • Establish ML platform governance for performance, cost optimization, and security compliance. • Partner with data platform team and solutions architects to solve data gaps or optimize data pipelines for advanced analytics. • Interacts, consults with and influences senior management at various levels of the organization, with various internal organizations (e.g. management, finance, marketing, academics, operations, legal). • Acts as liaison between the business operations and available data, working on various initiatives to maintain visibility and understanding of the data and decisions being made. • Promotes identification of best practices and fosters cross functional sharing of initiatives, innovation and best practices. • Performs other duties as assigned.