IQVIA

Customer Success Manager

• Ensures fulfilment of contract specifications and ad-hoc client requests to build client satisfaction, under supervision.
• Assists team members to build, establish, and maintain effective business relationships with clients, and proactively supports their needs as their day-to-day contact, possibly focused on a specific product or service.
• Ensures that the product deliverable meets the customer's specifications, including timeliness and quality through review and verification.
• Maintains effective internal communications with sales and other key account owners to keep all apprised of activities with the client.
• Deliver targeted reports as required.
• Monitors performance against existing service agreements and ensures client’s obligations are met.
• Carries out client research to ensure an in-depth understanding of the client, specific business line, and/or therapeutic market.
• Conducts product/services and process training for clients and other employees.
• Potentially conduct service meetings at client sites.
• Represents IQVIA on external client projects.
• Identifies and escalates potential risks or issues to the appropriate internal teams to ensure timely resolution.
• Provide feedback to product development teams based on client interactions to help improve future product offerings.

Customer Success Specialist – SaaS

Engineering Manager

• Own end‑to‑end delivery execution for Agentic AI solutions from Proof of Concept through Production
• Translate product scope and architectural direction into clear delivery plans, sprint cadence, and execution milestones
• Drive day‑to‑day engineering delivery, including dependency management
• Ensure delivery readiness across environments, CI/CD, quality gates, validation, and release governance
• Coordinate and orchestrate internal teams and vendors to deliver as a single, integrated delivery unit
• Partner closely with Product, Architecture, QA, Validation, and Program Management
• Provide transparent delivery reporting, proactively surface risks
• Support release and rollout execution
• Lead and mentor a multidisciplinary engineering team
• Act as delivery owner for vendor-contributed work
• Ensure vendor outputs meet IQVIA standards.

AI Engineering Manager – Delivery Lead

AI Engineer

• Lead hands‑on technical execution for Agentic AI capabilities across PoC, MVP, and Production, ensuring delivery and production‑grade quality.
• Translate architecture and product intent into a clear build plan (components, sequencing, integration approach, technical risks), and drive implementation through the team.
• Define and enforce engineering practices: coding standards, PR/review quality bar, branching strategy, and “done” criteria for agentic workflows (including testability and operational readiness).
• Implement and guide development of key agentic components such as orchestration logic, tool/API integrations, retrieval patterns, guardrails, and human‑in‑the‑loop flows, ensuring they are reliable and maintainable.
• Own technical unblock and troubleshooting: resolve complex defects, integration issues, performance bottlenecks, and environment/build pipeline problems.
• Drive release readiness from a technical standpoint: dependency readiness, versioning, deployment checks, rollback considerations, and operational handover inputs.
• Ensure vendor‑delivered technical work is aligned to standards through review, integration oversight, and quality controls.
• Collaborate with QA/Validation to ensure the solution is testable and traceable, with appropriate evidence generation through the SDLC (unit/integration/system testing hooks, logs, artifacts).
• Partner with the Solutions Architect to ensure adherence to enterprise patterns, security/privacy expectations, and architectural constraints, escalating gaps early and proposing practical implementation options.
• Post‑release, drive reliability and performance tuning, manage technical debt, and improve observability to support ongoing stability of agentic behaviors in production.

Senior AI Tech Lead

Data Engineer

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes supporting AI research, prototyping, and production use cases.
• Collaborate with AI scientists and engineers to translate data requirements into ingestion, transformation, and serving solutions.
• Apply data governance and security controls ensuring compliance, auditability, and protection of sensitive information.
• Monitor, troubleshoot, and resolve data pipeline failures, performance issues, and schema changes.
• Continuously improve reliability through testing, observability, documentation, and automation.
• Design and maintain efficient data models supporting analytics, ML workflows, and AI agent operations.

AI Data Engineer

Solutions Engineer

• Architect and deliver scalable, secure cloud-native systems across AWS and Azure.
• Lead architecture for AI-enabled and agentic applications, including LLM-based workflows.
• Rapidly build MVPs and prototypes to validate requirements and architectural decisions.
• Use AI-assisted development tools (e.g., Cursor) to accelerate prototyping and iteration.
• Engage stakeholders to define requirements, trade-offs, and architectural direction.
• Provide architectural guidance and mentorship across teams, serving as a collaborative partner to Tech Leads by working jointly on architectural direction and project delivery while offering hands-on support as needed.

AI Solutions Architect

Manager

• Provide centralized, global clinical trial oversight within FSP model
• Lead and coordinate global and regional clinical trial management activities
• Act as primary point of contact for stakeholders
• Drive trial delivery across full lifecycle
• Ensure milestones and deliverables are met and communicated
• Review and approve vendor deliverables and ensure vendor performance
• Ensure continuous inspection readiness
• Lead feasibility activities and provide recommendations
• Oversee trial documentation and coordinate data cleaning activities
• Lead teams across projects or programs and provide coaching

Trial Delivery Manager, Multi TA

Product Manager

• Lead execution-level product ownership of Agentic AI solutions across Proof of Concept, MVP, and Production
• Line-manage and develop Product Owners and/or Business Analysts
• Own and maintain the Agentic AI product backlog, translating business needs into clear, agent-ready user stories
• Define and enforce Definition of Ready and acceptance standards
• Drive backlog prioritization and refinement
• Partner with AI Product Managers to support MVP scope decisions
• Collaborate closely with Architecture, Engineering, and UX
• Act as primary execution-time product contact for engineering teams
• Own story acceptance and UAT
• Manage the post-release backlog for enhancements, defects, and agent tuning

Senior AI Product Owner Manager

Product Designer

• Embrace the Founder's Journey: Build with care and empathy and use your strong vision and unique taste to design products that differentiate in the market and resonate deeply with customers.
• Experience Strategy & Design Execution: Lead the design strategy and execution for the core Media OS customer journey. Own the conceptual development and delivery of all design artifacts, from user flows and wireframes to high-fidelity prototypes and into development.
• User-Centric Excellence: Drive Exceptional Experiences by deeply understanding customer outcomes, not just their needs. Conduct research to empathize with users, validate assumptions, and build products that solve meaningful problems. Analyze user feedback, leading to data-informed iterative design improvements.
• Data-Driven Design: Serve as the bridge between data and business outcomes. Translate complex healthcare data into user-friendly, easy to understand insights, ensuring seamless integration of features within Media OS.
• Code and Craft: Embrace a mindset of end-to-end ownership, taking responsibility from ideation through implementation. Proactively experiment with ideas, drive significant progress through small, consistent efforts, and bridge the gap between design and development by combining code and craft to foster innovation and adaptability.
• Cross-Functional Collaboration: Partner closely with product owners and engineering to prioritize design requests, define requirements, and remove obstacles. You'll be a champion of great design and actively contribute to KPI, UX outcomes, and success metrics for measuring design impact.
• Design Systems & Process Improvement: Drive and ensure consistency and adherence to our design system standards. Continually evaluate and champion improvements to our design processes, tools, and best practices to deliver user-validated experiences.

• Own end‑to‑end delivery execution for Agentic AI solutions
• Translate product scope and architectural direction into clear delivery plans
• Drive day‑to‑day engineering delivery and issue resolution
• Ensure delivery readiness across environments, CI/CD, quality gates
• Coordinate and orchestrate internal teams and vendors
• Provide transparent delivery reporting
• Support release execution from pilot to scale
• Lead and mentor a multidisciplinary engineering team
• Act as delivery owner for vendor contributed work

Machine Learning Engineer

• Lead the design and development of ML applications across our product portfolio, focusing on generative AI and large language model (LLM) solutions
• Provide architecture and shape coding standards
• Evangelize best practices for software engineering including design, development, and lifecycle maintenance
• Partner with multiple software engineering teams to encourage practices like code reusability, shared libraries, and UX-driven design
• Guide the transformation of machine learning research domain expertise into viable prototypes
• Help our Machine Learning Engineers build and train production-grade algorithms
• Research current and emerging industry tools, techniques, and algorithms
• Meet frequently with stakeholders, product managers, engineering managers, data scientists, and other engineers to distill technical and business impacting variables into strategic choices for developing and improving software products for customers
• Work with external customers as a consultant or solution Machine Learning Engineer
• Prepare and submit conference and journal articles

Senior Machine Learning Engineer

Inside Sales

• Work with Non-Pharma accounts/opportunities as needed
• Analyze, process, and triage in-bound customer leads
• Help drive early engagement by internet research and/or cold calling to identify new opportunities
• Likely to interact at C-Suite level
• Under the leadership of Marketing & Sales Management, continuously monitor status of leads and opportunities
• Grows and maintains existing customer relationships
• Demonstrates sound knowledge of IQVIA National Data Assets
• Align and collaborate across the organization
• Communicate directly with sales management and sales colleagues
• Analyze potential opportunities
• Create, monitor and update a tracking document for Non-Pharma
• Identify new sales opportunities and proactively coordinate the lead cultivation process
• Report all sales related activities in CRM system
• Serve as a liaison between the customer and technical and operational groups at IQVIA
• Perform other duties as assigned.

Account Manager, Inside Sales

Consultant

• Provides high quality, on-time input to client projects in the medical devices field.
• Assists with the review and analysis of client requirements or problems and assists in the development of client solutions.
• Assists in the development of detailed documentation and specifications.
• Performs quantitative or qualitative analyses to assist in the identification of client issues and the development of client specific solutions.
• Assists in the design and structure of presentations that are appropriate to the characteristics or needs of the audience.
• Proactively develops a basic knowledge of consulting methodologies and the MedTech market through the delivery of consulting engagements and participation in formal and informal learning opportunities.

Medical Device Consultant

Artificial Intelligence

• Serve as the primary day‑to‑day point of contact for client stakeholders on assigned engagements.
• Manage client expectations, communication, and delivery cadence, escalating risks and decisions as needed.
• Support engagement shaping activities, including contributing to proposals, workplans, and client presentations.
• Help translate high‑level client objectives into clear scopes, milestones, and delivery plans.
• Build trusted working relationships with client team members across clinical, data, and technology functions.
• Lead the end‑to‑end execution of 1–2 AI‑enabled client engagements at a time, ensuring delivery against scope, timeline, and quality expectations.
• Manage cross‑functional project teams including data scientists, engineers, clinicians, and analysts.
• Coordinate day‑to‑day activities such as sprint planning, backlog prioritization, dependency management, and delivery tracking.
• Oversee delivery of AI/ML solutions related to clinical development simulation, leveraging real‑world data (RWD), clinical trial data, and other structured and unstructured sources.
• Work closely with technical leads to ensure AI solutions are scientifically sound, technically robust, and client‑ready.
• Support development and application of IQVIA’s foundation AI simulation capabilities within client engagements.
• Identify risks, issues, and trade‑offs early, and proactively drive resolution.
• Contribute to the design and refinement of AI roadmaps, PoCs, ML pipelines, and deployment approaches within projects.
• Help operationalize emerging AI capabilities into repeatable delivery patterns and client‑ready solutions.
• Collaborate with third‑party technology partners and internal platform teams to support successful delivery.
• Provide coaching and guidance to junior team members, supporting their professional development on projects.
• Capture delivery learnings and feedback to help inform future offerings and engagement approaches.

AI Engagement Manager

• Own the execution-level product ownership of Agentic AI solutions across Proof of Concept, MVP, and Production
• Translate business needs and product intent into agent-ready user stories, workflows, and acceptance criteria
• Own and maintain the Agentic AI product backlog
• Define and enforce Definition of Ready and acceptance standards
• Drive backlog prioritization and refinement
• Collaborate closely with Architecture, Engineering, and UX
• Act as the primary execution-time product contact for engineering teams
• Own story acceptance and UAT
• Manage the post-release backlog for enhancements, defects, and agent tuning

AI Product Owner

• Lead the execution‑level product ownership of Agentic AI solutions across Proof of Concept, MVP, and Production, ensuring delivery of defined outcomes in regulated environments.
• Line‑manage and develop Product Owners and/or Business Analysts, setting clear expectations, providing coaching, performance management, and building consistent product execution capabilities.
• Own and maintain the Agentic AI product backlog, translating business needs into clear, agent‑ready user stories, workflows, and acceptance criteria.
• Define and enforce Definition of Ready and acceptance standards, ensuring stories are testable, evaluable, and suitable for autonomous and human‑in‑the‑loop agent workflows.
• Drive backlog prioritization and refinement, balancing agent capabilities, evaluation readiness, non‑functional requirements, and delivery constraints.
• Partner with AI Product Managers to support MVP scope decisions, ensuring feasibility, readiness, and alignment with value hypotheses and success KPIs.
• Collaborate closely with Architecture, Engineering, and UX to shape agent behavior, orchestration patterns, guardrails, and exception handling early in the lifecycle.
• Act as the primary execution‑time product contact for engineering teams, clarifying intent, resolving scope questions, and validating delivered outcomes.
• Own story acceptance and UAT, ensuring delivered agent behavior meets defined success criteria, quality expectations, and regulatory requirements.
• Manage the post‑release backlog for enhancements, defects, and agent tuning, informed by telemetry, user feedback, and operational insights.

Customer Success Specialist – SaaS

AI Engineering Manager – Delivery Lead

Senior AI Tech Lead

AI Data Engineer

AI Solutions Architect

Trial Delivery Manager, Multi TA

Senior AI Product Owner Manager

Product Designer

AI Engineering Manager – Delivery Lead

Senior Machine Learning Engineer

Account Manager, Inside Sales

Medical Device Consultant

AI Engagement Manager

AI Product Owner

AI Product Owner – Manager